PRONUNCIATION VARIATION SPEECH RECOGNITION WITHOUT NEW DICTIONARY CONSTRUCTION

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深解析雅思口语发音评分标准

我的托福雅思必过深度解析雅思口语发音评分标准如果你口语发音有问题，请你必须认认真真地读完下面的文字！Pronunciation发音，Grammar语法，vocabulary词汇，Fluency Coherence流利度和连贯度这四个评分标准是同等重要的〔equally important 〕各占25%。

其中想谈的就是这发音。

由于中国的英文教育(English learning)体系里就很少注重这发音的练习。

所以就导致了中国大局部雅思考生说话明显的Chinese style English speaking Pronunciation )。

毫不夸张地说，我甚至可以称之为〔hometown English Pronunciation 〕。

时下不少雅思口语考生无论是在面授课还是线上课〔online course〕都不乏有令人称道的所谓口语素材。

拿到素材后，自以为这下好了，终于有话讲了，可是残酷的事实证明了很多考生还是〔5-6〕份的分数。

为啥？为何？难道这万年的雅思口语5分，6分就真正的命中注定了么？！！！其实非也！！我们不相信宿命论!我们更不是宿命论者〔fatalist〕素材只是素材。

剩下的全靠你的外包装如何，语法，词汇后的就是英文发音。

你有无说话的范〔style〕。

且看那年雅思口语评分标准〔public version〕对于6分的英文版规定：Use a range of pronunciation features with mixed controlShows some effective use of features but this is not sustainedCan generally be understood throughout , though mispronunciation of individual words or sounds reduces clarity at times.翻译成中文如下：使用多种发音特点，但掌握程度不一。

Pronunciation variations in emotional speech

2. SUPRASEGMENTAL HIDDEN MARKOV MODELS
Suprasegmental hidden Markov models (SPHMM) permit the summarization of several states within a hidden Markov model into what we will call a suprasegmental state. These suprasegmental states allow the consideration of the observation sequence spanned by their constituent states, i.e., these suprasegmental states can look at the observation sequence through a larger window. In our application acoustic events are modeled using conventional hidden Markov states, while prosodic events at the phone, syllable, word, and utterance level are modeled using suprasegmental states. The basic idea of an SPHMM is given in Fig. 1. Prosodic information can not be observed at a rate which is used for acoustic modeling. Prosodic information applies, for example, to syllables, words, or phrases but can not be observed within a time window of 10ms, the time frame in which acoustic events are usually looked at. To combine acoustic and suprasegmental information we linearly combine acoustic and suprasegmental probabilities. That is, each time we leave a suprasegmental model, for example a phone or syllable, we add the log probability that the suprasegmental observations given in the speech signal were produced by this suprasegmental model to the log probability that the acoustic observations given in the speech signal were produced

英语该如何学好发音

英语该如何学好发音English pronunciation can be a tricky thing to master, especially for non-native speakers. However, with practice and dedication, anyone can improve their pronunciation skills. In this guide, we will discuss some tips and techniques that can help you enhance your English pronunciation.1. Practice regularly: Like any skill, improving your pronunciation requires consistent practice. Make it a habit to practice English pronunciation every day. You can do this by reading aloud, listening to English songs or podcasts, or even talking to native speakers.2. Focus on individual sounds: English has a wide range of vowel and consonant sounds that may not exist in your native language. Pay special attention to these sounds and practice them regularly. You can use online resources or pronunciation guides to help you identify and practice specific sounds.3. Mimic native speakers: One of the best ways to improve your pronunciation is to mimic native speakers. Listen carefully to how they pronounce words and sentences, and try to imitate them as closely as possible. This will help you develop a more authentic accent and improve your overall pronunciation.4. Use phonetic spelling: English spelling can be confusing, as many words are not pronounced the way they are spelled. To help you with this, use phonetic spelling to break down words into their individual sounds. This will make it easier for you to pronounce them correctly.5. Focus on stress and intonation: English is a stress-timed language, which means that certain syllables in a word are emphasized more than others. Pay attention to the stress patterns of English words and practice pronouncing them with the correct intonation. This will make your speech sound more natural and fluent.6. Use tongue twisters: Tongue twisters are a fun and effective way to practice your pronunciation skills. They help improve your articulation and clarity by challenging you to say difficult phrases quickly and accurately. Try practicing a few tongue twisters every day to improve your pronunciation.7. Record yourself: One of the best ways to monitor your progress is to record yourself speaking English. Listen to the recording and pay attention to areas where you need to improve. This will help you identify your weaknesses and track your pronunciation improvements over time.8. Seek feedback: Don't be afraid to ask for feedback from native speakers or language teachers. They can provide valuable insights on your pronunciation and offer tips on how to improve. Take their advice seriously and use it to refine your pronunciation skills.9. Practice with a language partner: Find a language partner or join a language exchange program to practice your pronunciation with a native speaker. This will give you the opportunity to practice real-life conversations and receive instant feedback on your pronunciation.10. Be patient and persistent: Improving your pronunciation takes time and effort, so be patient with yourself and don't get discouraged. Keep practicing regularly and don't be afraid to make mistakes. Remember, the more you practice, the better you will become at English pronunciation.In conclusion, improving your English pronunciation is a gradual process that requires dedication and practice. Follow the tips and techniques outlined in this guide, and you will gradually see improvements in your pronunciation skills. Remember, practice makes perfect, so keep practicing and don't give up!。

英语发音纯正的方法

英语发音纯正的方法## Mastering the Art of Native-Level English Pronunciation.Achieving impeccable English pronunciation goes beyond mere memorization of sounds and rules. It entails a comprehensive approach that encompasses an understanding of the language's intricate phonological system, consistent practice, and a keen ear for auditory feedback.1. Delve into the Intricacies of English Phonology:Comprehend the fundamental principles governing English pronunciation, including:Phonetics: The study of individual speech sounds, their production, and their transcription using symbols.Phonemes: The smallest units of sound that distinguish one word from another. For example, the sounds /p/ and /b/in "pat" and "bat."Syllables: The building blocks of words, consisting of a nucleus (usually a vowel) and optional consonants.Stress: The prominence or emphasis placed on certain syllables in a word.Intonation: The variation in pitch and volume that conveys meaning and emotions in speech.2. Practice with Dedication and Consistency:Repetition is paramount for perfecting pronunciation. Engage in regular practice exercises that target specific sounds, syllables, and intonation patterns.Pronunciation Drills: Utilize online resources, textbooks, or a language tutor to practice pronouncing individual sounds, common consonant clusters, and vowel combinations.Shadowing: Listen to native English speakers and repeat their speech verbatim, imitating their pronunciation, intonation, and rhythm.Reading Aloud: Read aloud a variety of texts, from simple passages to complex literary works. This exercise helps improve fluency and strengthens your ability to produce accurate sounds in context.3. Develop an Acute Ear for Native Pronunciation:Attuning your ear to native-like speech is essentialfor fine-tuning your pronunciation.Immersion: Surround yourself with English by watching movies and TV shows, listening to podcasts, and reading English literature.Feedback: Seek feedback from native English speakers or a language teacher to identify areas for improvement and adjust your pronunciation accordingly.Online Resources: Utilize speech analysis tools, such as Forvo or PronunciationTutor, to compare your pronunciation with native speakers.4. Tackle Common Pronunciation Challenges:Address specific pronunciation difficulties that English learners often encounter:Vowel Sounds: Distinguish between similar vowel sounds, such as /i/ (as in "beat") and /ɪ/ (as in "bit"), or /ʊ/ (as in "foot") and /ʌ/ (as in "but").Consonant Clusters: Master the pronunciation of challenging consonant clusters, such as /tr/ in "train,"/pl/ in "play," and /sk/ in "school."Linking and Elision: Learn to connect words smoothly in connected speech and elide certain sounds, such as the "h" in "what have."Word Stress: Identify the correct syllable stress inwords and avoid mispronunciations caused by incorrect stress placement.5. Seek Professional Guidance:Consider working with a qualified language teacher or speech therapist who specializes in English pronunciation. They can provide personalized instruction, tailored feedback, and valuable insights into the nuances of native-level pronunciation.Remember, achieving native-like English pronunciation is a journey that requires perseverance, dedication, and a passion for the language. Embrace the challenge, engage in consistent practice, and surround yourself with native English speakers to enhance your communication skills and elevate your confidence in using the language.。

Production domain modeling of pronunciation for visual speech recognition

PRODUCTION DOMAIN MODELING OF PRONUNCIATION FOR VISUAL SPEECH RECOGNITION Kate Saenko,Karen Livescu,James Glass,and Trevor Darrell {saenko,klivescu,jrg,trevor}@Computer Science and Artiﬁcial Intelligence LaboratoryMassachusetts Institute of Technology32Vassar Street,Cambridge,MA02139,USAABSTRACTArticulatory feature models have been proposed in the au-tomatic speech recognition community as an alternative to phone-based models of speech.In this paper,we extend this approach to the visual modality.Speciﬁcally,we adapt a recently proposed feature-based model of pronunciation variation to visual speech recognition(VSR)using a set of visually-salient features.The model uses a dynamic Bayes-ian network to represent the evolution of the feature streams.A bank of SVM feature classiﬁers,with outputs converted to likelihoods,provides input to the DBN.We present pre-liminary experiments on an isolated-word VSR task,com-paring feature-based and viseme-based units and studying the effects of modeling inter-feature asynchrony.1.INTRODUCTION Traditionally,visual speech is modeled as a single stream of contiguous units,each corresponding to a hidden phonetic state.These units are deﬁned by mapping several visually similar phonemes to a single viseme.However,a many-to-one mapping does not always exist,as the appearance of the mouth during phone production can be heavily inﬂuenced by the surrounding context.This often occurs when articu-lators not primarily involved in the production of the current phone evolve asynchronously from the primary articulators. Figure1shows an example of such de-synchronization in a segment taken from the center of the utterance“promote birth”.Note that during the/t/segment,the lips,which would normally be in a medium-open position,are com-pletely closed due to the upcoming bilabial phoneme.One way to capture such variability is by using context-dependent units.However,visual coarticulation effects such as the one described above can span three or more phonemes, requiring a large number of models.This leads to an inef-ﬁcient use of the training data,and cannot anticipate new variations.Alternatively,we can break the assumption that This research was supported by ITRI and by DARPA under SRI sub-contract No.03-000215.Fig.1.Mouth images aligned with the corresponding phoneme sequence. visemes are the basic building blocks of visual speech and instead model articulatory events,which we believe are the more natural visual units.From the point of view of speech production,each sound can be described by a unique combination of several artic-ulator states,or articulatory features(AFs),such as:the presence or absence of voicing,the position of the tongue body and tongue tip,the opening between the lips,and so on.A word consists of a number of(not necessarily syn-chronous)sequences of articulatory targets.Conventional speech models make the simplifying assumption that a word can be broken up into phonemes,each of which is an atomic unit.The articulatory approach offers a moreﬂexible and parsimonious architecture.For example,the visual speech segment in Figure1can be explained as the de-synchroniza-tion of the lips from the remaining articulators.Although similar pronunciation models have been used in modeling spontaneous acoustic speech[9],to the best of the authors’knowledge,this is theﬁrst application of the multi-stream articulatory feature approach in the visual domain.In the following sections,we present a visual speech recognition framework that models visual speech in terms of the under-lying articulatory processes.2.VISUAL ARTICULATORY FEATUREDETECTIONWe treat articulatory features as the hidden states underlying the surface visual observations[12],and learn them using a supervised learning approach.An observed feature vector is used as the input to a statistical classiﬁer,which outputs the hidden articulatory feature labels.A preprocessing step extracts the observed feature vector from the input image. In principle,each articulatory feature classiﬁer could usedifferent observation-level measurements.For example,theclassiﬁer for“lip rounding”could take motion vectors asinput,while the“dental”classiﬁer could use color input.We assume a set of training examples with images ofmouths and the corresponding articulatory feature labels;each image has several discrete labels,one for each AF.Inpreliminary experiments,we have found that support vec-tor machines(SVMs)outperform Gaussian Mixture Modelson the task of articulatory feature classiﬁcation for a singlespeaker,and have therefore chosen to use SVM classiﬁers.In dealing with the visual modality,we are obviouslylimited to modeling the visible articulators.As a start,weare using features associated with the lips,since they are al-ways visible in the image:LIP-OP(closed,narrow,medium,wide),LIP-RND(rounded,unrounded)and LAB-DEN(la-bio-dental,not labio-dental).This ignores other articulatorsthat might be distinguishable from the video,such as thetongue and teeth;we plan to incorporate these in the future.Note that the standard formulation of SVM classiﬁca-tion produces a hard decision(the class label).However,inorder to not lose information by forcing a decision at thisearly stage,we produce soft decisions in the form of poste-riors P(F t=f|X t=x),where X t is the observation at time t and F t is a particular AF.SVM outputs are convertedto posterior probabilities using the implementation of thesigmoidal mapping described in[3].Furthermore,since ourrecognizer uses a generative model,it is more natural to uselikelihoods than posteriors,so we convert the posteriors to(scaled)likelihoods using P(X t=x|F t=f)∝P(F t= f|X t=x)/P(F t=f).3.A DYNAMIC BAYESIAN NETWORK FORFEATURE-BASED VSROur recognition model is based on the work described in[8, 9].The model generates,for each word in its vocabulary, all sequences of AF values that are possible realizations of that word,along with the probabilities of those realizations. While a word may have only one or two baseform(dictio-nary)pronunciations,such as either→{/iy dh er/,/ay dh er/},there may be thousands of AF combinations that are possible realizations of the word.In order to take advan-tage of the semi-independent evolution of the AF streams—in other words,the factorization of the AF state space—we implement the model as a dynamic Bayesian network (DBN).Figure2shows(a slightly simpliﬁed version of)one frame of the DBN used in our experiments.Conditioned on the identity of the word—or,in the case of words with more than one baseform,conditioned on the word and the baseform—the model essentially consists of three parallel HMMs,one per AF,where the joint evolution of the HMM states is constrained by synchrony requirements as we de-scribe below.Given a word,the model generates its realizations as follows.First,a baseform is drawn from the set of allowed baseforms for the word.This baseform pronunciation de-ﬁnes a set of target feature value trajectories,one for each AF F.The AFs then proceed through their trajectories,pos-sibly at different rates(i.e.asynchronously).In Figure2,i F t is an index into the trajectory of feature F at time frame t;i.e.,if F is in the n th state of its trajectory at time t,then i F t=n.U F t is the underlying target value corresponding to this state.We deﬁne the degree of asynchrony between two features F1and F2at time t as|i F1t−i F2t|.This asynchrony is not completely unconstrained:Sets of trajectories that are more“synchronous”may be more probable than less“syn-chronous”ones,and we impose a limit on the maximum asynchrony between sets of features.The probabilities of varying degrees of asynchrony are given by the distributions of the async j variables.checkSync j t simply checks that the degree of asynchrony between its parent features is in fact equal to async j t:It is always observed with value1and its distribution isPcheckSync j t=1|async j t,i F1t,i F2t=1⇐⇒|i F1t−i F2t|=async j t,and0otherwise,where i F1t and i F2t are the indices of the features corresponding to checkSync j t.1In the model of Figure2,async1t is the degree of asynchrony between LIP-RND and LIP-OP,and async2t is the degree of asynchrony between LIP-OP and LAB-DEN.Finally,the surface value S F t that is actually produced by the speaker at time t for feature F may differ from the underlying value U F t,usually due to undershoot(e.g.in-complete lip closure for a/b/)or context effects.For the ex-periments in this paper,however,we focus on asynchrony modeling and assume that S F t=U F t for all t,F.Fig.2.One frame of a DBN for feature-based VSR.All variables are discrete-valued.U F t are the underlying feature values,and S F t are the surface values.i F t is an index into the state sequence of feature F.Edges without parents/children in theﬁgure connect the i F t in adjacent frames.1A simpler structure could be used for decoding,but it would not allow for EM training of the asynchrony probabilities(see[8]).In order to incorporate the likelihoods computed from the SVM outputs,we use the Bayesian network mechanism of soft evidence[1].This is used when a variable is not ob-served but we have some information that causes us to favor some values over others;this is exactly what the SVM out-puts tell us about the AF values.Soft evidence allows us to combine a generative model with likelihoods computed by any means,including discriminative classiﬁers such as SVMs.This can be done by adding,for each articulatory feature F,a“dummy”evidence variable E F t,whose value is always1and whose distribution is constructed so that P(E F t=1|S F t=f)is proportional to the likelihood P(X t= x|S F t=f)computed from the SVM discriminant values.Thus far we have used this model for isolated-word recog-nition,which amounts toﬁnding the word that maximizes the probability of the observations.The parameters of the distributions in the DBN can be learned from SVM soft ev-idence outputs for a set of training data,for example using the Expectation-Maximization(EM)algorithm[5].Both tasks can be accomplished using standard DBN inference algorithms[10].In the proof-of-concept experiments de-scribed below,however,a very small data set was used and no DBN parameter learning was done;the DBN parameters were set manually,using linguistically plausible values.4.EXPERIMENTS AND RESULTSWe have conducted pilot experiments to investigate several questions.First,we would like to compare the effects of using feature-based versus viseme-based classiﬁers,as well as of using a feature-based versus viseme-based pronuncia-tion model.A viseme-based pronunciation model is a spe-cial case of our DBN,in which the features are constrained to be completely synchronous(i.e.async j t is identically0) and no feature changes are allowed(i.e.S F t=U F t).Us-ing viseme classiﬁers with a viseme-based pronunciation model results in essentially the conventional viseme-based HMM that is used in most VSR systems.In order to use a feature-based pronunciation model with viseme classiﬁers, we use a many-to-one mapping from surface features(S F t) to visemes.Also,since we do not have ground truth artic-ulatory feature labels,we investigate how sensitive the sys-tem is to the quality of the training labels in terms of both feature classiﬁcation and word recognition.In order to facil-itate quick experimentation,these initial experiments focus on an isolated-word recognition task using a small data set and,as previously mentioned,manual settings for the(small number of)DBN parameters.4.1.Data and Visual Signal PreprocessingFor these initial experiments,we used21utterances taken from a single speaker in A VTIMIT[7],a corpus of audio-visual recordings of subjects reading phonetically balanced sentences with a vocabulary of1793words.Of these,10ut-terances were used for training and11for testing.To simu-SVM type LIP-OP LIP-RND LAB-DEN viseme Forced train(%)44(84)63(84)50(99)33(71) Manual train(%)59(83)78(87)87(99)N/A Table1.Classiﬁer accuracies for the feature and viseme SVMs,aver-aged over the N classes for each SVM:acc=1NNi=1acc(class i).Chance performance is1N.The numbers of classes are:4for LIP-OP,2 for LIP-RND and LAB-DEN,and6for the viseme SVM(consisting of those combinations of feature values that occur in the forced transcriptions).late the isolated-word task,utterances were split into words, resulting in a70-word test set.Each visual frame was also manually transcribed with the three AFs.The raw video stream was preprocessed byﬁrst extract-ing37x54pixel mouth regions from the image sequence and converting them to grayscale(see Figure1).Then,a DCT transform was applied to each image to obtain a set of 1998coefﬁcients,of which the900highest-frequency coef-ﬁcients were retained.The dimensionality was further re-duced via PCA,with the top100PCA coefﬁcients used as theﬁnal observation vector.4.2.ClassiﬁcationA radial basis function(RBF)kernel SVM classiﬁer was trained for each of the three AFs using LIBSVM[3].To ﬁnd optimal values of the SVM parameters,cross-validation was performed on the training set.In order to study the ef-fects of training label accuracy,we considered two cases.In one case,phoneme labels from an acoustic forced transcrip-tion were converted to AF training labels using a determin-istic mapping.In the other case,manual articulatory feature transcriptions were used.Theﬁrst three columns of Table1 show the resulting classiﬁcation rates for each feature.Av-erage per-class accuracies are reported to account for the uneven distribution of classes in the data,with the total per-centage of correctly classiﬁed frames shown in parentheses. As we can see,manual labels result in higher per-class ac-curacy.The last column shows the performance of a viseme SVM trained from forced transcriptions.4.3.Word ranking experimentsBecause of the extreme difﬁculty of this task—lipreading isolated words excised from continuous speech with a rel-atively large vocabulary—we cannot expect to obtain rea-sonable word recognition error rates.Instead,we perform a word ranking experiment:For each spoken word in the test set,we compute the probability of each word in the vocabu-lary and rank the words based on their relative probabilities. Our goal is to obtain as high a rank as possible for the cor-rect word.Performance is evaluated using both the mean rank of the correct word over the test set and the entire dis-tribution of the correct word ranks.We used the Graphical Models Toolkit[2]for all DBN computations.In the models with asynchrony,LIP-RND and LIP-OP were allowed to desynchronize by up to oneMean rank,Mean rank, Classiﬁer unit sync model async modelViseme281.6262.7(.1)Feature,forced train216.9(.03)209.6(.02)Feature,manual train165.4(.0005)149.4(.0001)Feature,oracle113.0(2×10−5)109.7(3×10−5)Table2.Mean rank of the correct word in several conditions. index value(one phoneme-sized unit),as were LIP-OP and LAB-DEN.Table2summarizes the mean rank of the cor-rect word in a number of experimental conditions,and Fig-ure3shows the entire empirical cumulative distribution func-tions(CDFs)of the correct word ranks in several of these conditions.In the CDF plots,the closer the distribution is to the top left corner,the better the performance.We consider the baseline system to be the viseme-based HMM,i.e.the synchronous pronunciation model using the viseme SVM.In these experiments,the asynchronous pronunciation model always outperforms the synchronous one,regardless of the type of classiﬁers used.This may seem counterin-tuitive when viseme classiﬁers are used;however,certain apparently visemic changes may be caused by feature asyn-chrony;e.g.a/k/followed by an/uw/may look like an/ao/ because of LIP-OP/LIP-RND asynchrony.Next,the forced train vs.manual train comparison suggests that we could ex-pect a sizable improvement in performance if we had more accurate training labels.While it may not be feasible to manually transcribe a large training set,we may be able to improve the accuracy of the training labels using an itera-tive training procedure,in which we alternate training the model and using it to re-transcribe the training set.To show how well the system could be expected to perform if we had ideal classiﬁers,we replaced the SVM soft evidence with likelihoods derived from our manual transcriptions.In this “oracle”test,we assigned a very high likelihood(≈0.95)to feature values matching the transcriptions and the remain-ing likelihood to the incorrect feature values.Table2also gives the signiﬁcance(p-value)of the mean rank differences between each model and the baseline(according to a one-tailed paired t-test[13]).The differences between each syn-chronous model and the corresponding asynchronous model are not signiﬁcant(p≥.1on this test set),but all feature-based models are signiﬁcantly better than the baseline.5.SUMMARY AND FUTURE WORKWe have shown,for a limited VSR scenario,that a recog-nizer that models the articulatory asynchrony inherent in the human speech production system can outperform one that does not.We plan to continue testing this model on more data and in comparison with more realistic viseme-based baselines.We are also interested in applying this model to the problem of audio-visual fusion.Most state-of-the-art audio-visual speech recognizers model the asynchrony be-tween the audio and visual streams[6].However,the fusionFig.3.CDF of the correct word’s rank,using the visemic baseline and the proposed feature-based model.The rank r ranges from1(highest)to the vocabulary size(1793).is done at the level of the phoneme/viseme.We believe that the feature is a more natural level for audio-visual fusion. This has been previously suggested[11],but to our knowl-edge has not been attempted.The structure we have used can be naturally extended to perform this type of fusion;all that is required is a complementary set of classiﬁers for the acoustically-salient features,such as voicing and nasality, and the corresponding additional variables in the DBN.6.REFERENCES[1]J.Bilmes,“On soft evidence in Bayesian networks,”U.WashingtonDept.of EE Tech.Report No.UWEETR-2004-0016,2004.[2]J.Bilmes and G.Zweig,“The Graphical Models Toolkit:An opensource software system for speech and time-series processing,”in Proc.ICASSP,2002.[3] C.Chang and C.Lin,“LIBSVM:A library for sup-port vector machines,”2001.Software available at .tw/cjlin/libsvm.[4]T.Dean and K.Kanazawa,“A model for reasoning about persistenceand causation,”Computational Intelligence,5:142–150,1989. [5] A.P.Dempster,ird,and D.B.Rubin,“Maximum likeli-hood from incomplete data via the EM algorithm,”Journal of the Royal Statistical Society,39:1–38,1977.[6]G.Gravier,G.Potamianos,and i,“Asynchrony modeling foraudio-visual speech recognition,”in Proc.Human Language Tech-nology Conference,2002.[7]T.J.Hazen,K.Saenko,,and J.Glass,”A segment-basedaudio-visual speech recognizer:Data collection,development,and initial experiments,”in Proc.ICMI,2005.[8]K.Livescu and J.Glass,“Feature-based pronunciation modeling forspeech recognition,”in Proc.HLT/NAACL,2004.[9]K.Livescu and J.Glass,“Feature-based pronunciation modelingwith trainable asynchrony probabilities,”in Proc.ICSLP,2004. [10]K.P.Murphy,Dynamic Bayesian Networks:Representation,Infer-ence and Learning.Ph.D.thesis,U.C.Berkeley CS Division,2002.[11]P.Niyogi,E.Petajan,and J.Zhong,“A feature based representationfor audio visual speech recognition,”in Proc.Aud.Vis.Sp.Conf., 1999.[12]K.Saenko,J.Glass,and T.Darrell,“Articulatory features for robustvisual speech recognition,”in Proc.ICMI,2005.[13] E.W.Weisstein,“Paired t-Test,”from MathWorld–A Wolfram WebResource./Pairedt-Test.html。

语言学知识_语音学

语言学知识_语音学语音学一．语音学（Phonetics）定义：语音学主要研究的是言语语音的特点，并提供对它们进行描写、分类和转写方法的科学。

言语表达过程通常分为三个部分：说话者、空气媒介以及受话者，因此语音学可以分为三个分支：1.发音语音学（articulatory phonetics）2.声学语音学（acoustic phonetics）3.听觉语音学（auditory phonetics）二．发音器官（Speech Organ）：人类的发音器官主要包含三个区域，分别是咽腔（pharyngeal cavity），口腔（oral cavity）以及鼻腔（nasal cavity）。

三．语言语音的正字表征（Orthographic Representation）：正字表征（Orthographic Representation）指的是某一种语言的标准化书写系统，也指规定性的拼写系统。

19世纪末诞生了国际音标（IPA），它同时为学者们提供了另外一套符号，称为变音符（diacritics）。

现今的音标分为两种：一种是宽式音标（broad transcription），另一种是严式音标（narrow transcription）。

宽式音标（broad transcription）指的是一种使用字母来表示音素的方法；而严式音标（narrow transcription）指的是一种使用字母和变音符（diacritics）同时来表示音素及细微语音特征的方法。

四．英语语音的分类（Classification of English Speech Sounds）：英语中语音有两种分类方式，首先可以分为分为元音（vowel）和辅音（consonant）。

其次，英语中的语音还可以分为清音（voiceless sound）和浊音（voiced sound）。

1. 元音（vowel）和辅音（consonant）：1.1 什么是元音元音指的是，在发音过程中，气流通过声道时不受到任何阻碍。

英语学习过程中容易忽视的语音重要性

英语学习过程中容易忽视的语音重要性English learners often focus on improving their vocabulary, grammar, and reading skills while neglecting the importance of pronunciation. However, mastering English pronunciation is crucial for effective communication. In this article, we will explore the often overlooked significance of phonetics in the process of learning English.1. IntroductionThe ability to pronounce English words accurately is essential for being understood and conveying our thoughts clearly. Neglecting pronunciation can lead to miscommunication and hinder one's language proficiency. Therefore, it is important to recognize the significance of phonetics in the English learning journey.2. The Role of Phonetics in CommunicationPhonetics refers to the study of speech sounds, including how they are produced, transmitted, and perceived. Understanding phonetics helps learners to articulate sounds correctly and distinguish between different phonemes. This, in turn, enables effective communication and comprehension.3. Enhancing Listening SkillsAcquiring good pronunciation skills enhances listening comprehension. By accurately pronouncing words and understanding their sounds, learners can better comprehend spoken English. Mastery of English phonetics allows learners to follow conversations, lectures, and presentations more efficiently.4. Improving Speaking SkillsThe ability to produce English sounds accurately is essential for effective spoken communication. Pronouncing words correctly helps convey intended meanings and reduces the chances of ambiguity. Moreover, proper pronunciation makes speech more natural and native-like, which increases overall fluency and confidence.5. Overcoming Communication BarriersAccurate pronunciation helps overcome communication barriers. When learners have a strong command of English phonetics, they can overcome speech and accent barriers, facilitating communication with native speakers and other English learners. This contributes to creating a more inclusive and diverse language learning environment.6. Enhancing IntelligibilityProper pronunciation increases intelligibility, making it easier for others to understand what learners are saying. Clear and accurate pronunciation reduces the need for repetition and enhances efficiency in oral communication. It promotes effective dialogue and eliminates any misinterpretation caused by incorrect pronunciation.7. Developing Phonemic AwarenessPhonemic awareness, the ability to recognize and manipulate individual sounds within words, is crucial for verbal communication. By focusing on phonetics, learners develop phonemic awareness, enabling them to identify and differentiate between sounds. This skill is particularly important in English, considering its complex sound system.8. Avoiding MiscommunicationNeglecting pronunciation can result in miscommunication and misunderstandings. Mispronouncing certain sounds or stressing syllables incorrectly can completely alter the meaning of words. By paying attention to phonetics, learners can reduce the chances of miscommunication and convey their intended messages accurately.9. Improving AccentWhile accents are a natural part of language diversity, developing a clear accent is desirable for effective communication. By studying phonetics, learners can work towards reducing their accents and making their speech clearer and more intelligible to a wider range of listeners.10. ConclusionIn the process of learning English, it is crucial not to overlook the importance of phonetics. By focusing on pronunciation, learners can enhance their listening and speaking skills, overcome communication barriers, improve intelligibility, and avoid miscommunication. Therefore, a comprehensive language learning approach should include regular practice and study of English phonetics to ensure effective communication and language proficiency.。

专业的语音发音技巧

专业的语音发音技巧Language is a powerful tool for communication, and pronunciation plays a crucial role in ensuring that our intended messages are effectively conveyed. Mastering professional pronunciation skills can greatly enhance our ability to express ourselves clearly and confidently. In this article, we will explore some essential techniques and tips for improving our language pronunciation.1. Develop awareness of phoneticsPhonetics is the study of the sounds used in language. By developing an understanding of basic phonetics principles, we can identify and differentiate between different sounds. This awareness is essential for improving our pronunciation. For example, learning about vowel and consonant sounds, syllable stress, and intonation patterns can significantly enhance our ability to reproduce accurate pronunciation.2. Practice with native speakersOne of the best ways to improve pronunciation is to practice with native speakers. Engaging in conversations and interacting with those who have a strong command of the language allows us to observe and imitate their pronunciation. Native speakers can provide valuable feedback and correct any errors or mispronunciations we may have. This practice enables us to refine our pronunciation skills and develop a more natural-sounding speech.3. Utilize technology and resourcesIn the digital age, we are fortunate to have access to various technological tools and resources that can aid our pronunciation practice. Online platforms, mobile applications, and software programs offer interactive exercises, audio clips, and pronunciation guides to help us master specific sounds or words. These resources can be utilized as valuable aids in our daily practice to refine our pronunciation skills.4. Record and self-evaluateRecording ourselves while speaking or reading aloud allows us to listen to our pronunciation and identify areas for improvement. Paying attention to our articulation, stress patterns, and intonation can help us pinpoint any weaknesses. By self-evaluating, we become more aware of our pronunciation errors and can take steps to correct them. This process of self-reflection and improvement is crucial in honing our pronunciation skills.5. Mimic native speakersMimicking native speakers is an effective technique for developing accurate pronunciation. By closely observing and imitating their mouth movements, stress patterns, and intonation, we can acquire a better understanding of how specific sounds are produced. Moreover, mimicking native speakers helps us internalize the correct pronunciation naturally and effortlessly. This emulation process should be complemented by regular practice to ensure long-term improvement.6. Seek professional guidanceFor those who desire a more structured approach to improving their pronunciation, seeking professional guidance through language courses orhiring a language tutor can be highly beneficial. Experienced instructors can provide personalized feedback, address specific pronunciation challenges, and offer targeted exercises to target problem areas. Their expertise and guidance can significantly accelerate our progress in developing professional pronunciation skills.7. Practice regularlyLike any skill, consistent practice is key to mastering pronunciation. Regular practice sessions, even for short periods, can yield significant improvements over time. Incorporating pronunciation exercises into our daily routine, such as reading aloud, watching videos or movies in the target language, and engaging in conversations, will help solidify newly acquired pronunciation skills.ConclusionMastering professional pronunciation skills requires dedication, practice, and a commitment to continuous improvement. By developing awareness of phonetics, practicing with native speakers, utilizing technology and resources, self-evaluating, mimicking native speakers, seeking professional guidance, and engaging in regular practice, we can refine our pronunciation and become more effective communicators. Remember, pronunciation is a journey, and with determination and perseverance, success is within reach.。

英语发音特点及注意事项

英语发音特点及注意事项The Unique Features and Considerations of English Pronunciation.English pronunciation, being the foundation ofeffective communication in the language, holds significant importance. It is not merely about speaking words but about expressing them correctly to convey the intended meaning. The beauty of English lies in its rhythm, melody, and the blend of sounds that create words and sentences. Let's delve into the unique features and considerations of English pronunciation.1. Vowel Diversity.English is renowned for its rich vowel sounds, often called the "vowel sounds of the rainbow" due to their diverse qualities. From the short and crisp /æ/ in "cat" to the long and drawn-out /i:/ in "sea," English has a wide range of vowel sounds. Mastering these sounds is crucialfor accurate pronunciation.2. Consonant Variations.English consonants, too, come with their unique challenges. Some consonants, like /θ/ in "think" and /ð/ in "this," are unique to English and may be unfamiliar to speakers of other languages. Other consonants, like /r/, can be either voiced or unvoiced, depending on the context. Understanding and practicing these consonant variations is key to fluent pronunciation.3. Stress and Rhythm.English is a stress-timed language, meaning that words and phrases are grouped into stressed and unstressed syllables. Stressing the right syllable in a word or phrase is essential for clarity and comprehension. For instance, the word "con'tent" is pronounced with stress on the first syllable, while "con'text" has stress on the second syllable. Mastering the rhythm and stress patterns of English can significantly enhance communication skills.4. Linking Sounds.In English, words are often linked together through a process called liaison or linking. This involves gliding from one word to the next, often by changing the ending consonant of one word to match the beginning vowel of the next. For example, in the phrase "an apple," the /l/ sound in "an" is linked to the /æ/ sound in "apple," resulting in a smoother flow of speech.5. Intonation.Intonation is the rise and fall of the voice when speaking, which is crucial for conveying meaning and emotion in English. Different intonation patterns can change the entire meaning of a sentence. For instance, a rising intonation at the end of a sentence can suggest a question, while a falling intonation can indicate a statement. Understanding and practicing different intonation patterns is essential for effective communication.6. Regional Variations.English is spoken across the globe, resulting in a range of regional variations and accents. While some accents may be more widely recognized or prestigious, it's important to respect and embrace the diversity of accents. Understanding and adapting to different accents can enhance cross-cultural communication.Considerations for Improving Pronunciation.1. Regular Practice: Pronunciation skills require regular practice and dedication. Engage in daily speaking activities, such as reading aloud, listening to native speakers, and practicing conversations.2. Listen and Mimic: Listen to native speakers and mimic their pronunciation. Pay attention to their stress patterns, intonation, and linking sounds.3. Record Yourself: Recording yourself speaking andcomparing it to native speakers' pronunciation can help identify areas for improvement. Use this feedback to adjust your pronunciation.4. Seek Feedback: Seek feedback from native speakers or teachers to identify areas where your pronunciation needs improvement. Accepting constructive criticism can help you refine your skills.5. Maintain Patience and Persistence: Perfecting pronunciation can take time and effort. Be patient with yourself and keep practicing to see improvement.In conclusion, English pronunciation is an essential component of effective communication in the language. Understanding its unique features and considering the above-mentioned points can help speakers improve their pronunciation and enhance their cross-cultural communication skills. With regular practice and dedication, anyone can achieve fluent and accurate English pronunciation.。

英语如何利用学习语音工具提高发音准确性

英语如何利用学习语音工具提高发音准确性IntroductionBeing able to pronounce English words accurately is an essential aspect of language learning. A good pronunciation not only helps in effective communication but also boosts confidence. To achieve better pronunciation, learners can make use of various phonetic tools and techniques. This article explores different ways in which English learners can leverage these resources to enhance their pronunciation skills.1. Phonetics and PhonologyPhonetics is the study of sounds in human speech, while phonology focuses on the patterns and rules governing these sounds. A solid understanding of phonetics and phonology is crucial for improving pronunciation. Language learners should familiarize themselves with the different phonetic symbols and their corresponding sounds. Tools such as phonetic charts and phonetic dictionaries, available online or in books, can be immensely helpful in this context.2. Pronunciation Practice AppsTechnology has revolutionized language learning, and there are now numerous pronunciation practice apps available. These apps provide learners with interactive exercises, audio examples, and real-time feedback. Some popular options include ELSA Speak, Sounds: The Pronunciation App, and Speak English Fluently. Utilizing these apps regularly can help learners develop muscle memory and improve their pronunciation accuracy.3. Online Pronunciation CoursesThere are several online platforms offering comprehensive pronunciation courses. These courses are designed to address specific pronunciation difficulties faced by learners. They focus on individual sounds, stress patterns, intonation, and syllable divisions. Websites such as Udemy, Coursera, and FluentU offer a wide range of pronunciation courses suitable for learners of various proficiency levels.4. Interactive Online ToolsVarious interactive websites provide learners with tools for practicing specific aspects of pronunciation. For instance, Forvo is an online pronunciation dictionary that allows learners to hear native speakers pronounce words and phrases. Easy Pronunciation offers animated videos that demonstrate the correct mouth movements for producing specific sounds. Practicing with such tools can significantly enhance pronunciation accuracy.5. English Language Learning SoftwareLanguage learning software, such as Rosetta Stone and Duolingo, often includes pronunciation exercises and interactive lessons. These tools integrate listening, speaking, and pronunciation practice into one comprehensive package. Learners can use such software to improve their pronunciation while also gaining a deeper understanding of English grammar, vocabulary, and sentence structure.6. Speech Recognition TechnologySpeech recognition technology is becoming increasingly sophisticated and can now provide accurate feedback on pronunciation. Voice recognition apps like Google Assistant and Siri can recognize spoken words and assist learners in assessing their pronunciation accuracy. Engaging with such technology on a regular basis can help learners identify their weaknesses and work on improving them.ConclusionImproving English pronunciation requires consistent practice and exposure to authentic language resources. The use of phonetic tools, pronunciation practice apps, online courses, interactive online tools, language learning software, and speech recognition technology can significantly aid learners in their journey towards better pronunciation. By utilizing these resources, learners can develop a more accurate and confident speaking style, ultimately enhancing their overall English language skills.。

如何学好英式英语发音

如何学好英式英语发音Mastering British English pronunciation can be a challenging task for those who are not native speakers. However, with dedication, practice, and the right resources, it is possible to significantly improve your pronunciation skills. In this article, we will discuss some tips and strategies to help you learn British English pronunciation effectively.1. Understand the BasicsBefore delving into the specifics of British English pronunciation, it is essential to understand the basic principles that govern the language. British English is characterized by its use of Received Pronunciation (RP), which is considered the standard accent in the UK. RP is known for its clear enunciation, lack of regional accents, and precise pronunciation of sounds.In British English, there are 44 phonemes, which are the basic sounds that make up the language. These phonemes are divided into vowels and consonants, each of which has its own unique pronunciation. It is crucial to familiarize yourself with the different phonemes and their corresponding sounds to improve your pronunciation skills.2. Listen and Immerse YourselfOne of the best ways to improve your British English pronunciation is to listen to native speakers and immerse yourself in the language. By listening to British English speakers, you can familiarize yourself with the rhythm, intonation, and pronunciation of the language. You can do this by watching British TV shows, listening to British radio stations, or practicing with language learning apps that offer audio resources.Additionally, consider engaging with native speakers through language exchange programs or online language forums. By interacting with native speakers, you can practice your pronunciation in a natural setting and receive feedback on your accent.3. Practice PhoneticsTo improve your British English pronunciation, it is essential to practice phonetics, which is the study of speech sounds. Phonetics can help you understand the different sounds of British English and learn how to produce them accurately. There are several online resources and phonetic charts available that can help you identify and practice the different phonemes of British English.One effective way to practice phonetics is through mimicking native speakers. Choose a British English speaker whose accent you admire and try to imitate their pronunciation. Pay attention to the way they articulate sounds, the stress they place on certain words, and the rhythm of their speech. By practicing phonetics in this way, you can develop a more authentic British English accent.4. Focus on Vowels and ConsonantsIn British English, both vowels and consonants play a vital role in pronunciation. It is important to familiarize yourself with the different vowel and consonant sounds of British English and practice producing them accurately. Pay special attention to vowel sounds, as they can vary greatly depending on the word and context.When practicing vowels, focus on the length, pitch, and quality of the sound. For example, the vowel sound in the word "cat" is pronounced differently from the vowel sound in the word "cut." By paying attention to these subtle differences, you can improve your British English pronunciation.Similarly, practice consonant sounds by paying attention to the way the sounds are produced in the mouth. For example, the sound of the letter "t" in the word "tea" is produced by placing the tip of the tongue against the alveolar ridge. By understanding the articulation of consonant sounds, you can improve your pronunciation accuracy.5. Work on Stress and IntonationIn British English, stress and intonation play a crucial role in communication. Stress refers to the emphasis placed on certain syllables in a word or phrase, while intonation refers to the rise and fall of pitch in speech. By mastering stress and intonation patterns, you can enhance your British English pronunciation and sound more natural.Pay attention to the stress patterns in British English words and phrases. In general, stress tends to fall on the first syllable of a word, but there are exceptions to this rule. Practice stressing the correct syllables in words and sentences to improve your pronunciation accuracy.Additionally, work on intonation patterns by practicing the rise and fall of pitch in speech. Intonation can convey meaning, emotion, and attitude in communication, so it is important to master these patterns in British English. Practice listening to native speakers and imitating their intonation patterns to improve your pronunciation skills.6. Use Language Learning AppsThere are several language learning apps available that can help you improve your British English pronunciation. Apps such as Duolingo, Rosetta Stone, and Babbel offer interactive exercises and pronunciation drills that can help you practice the sounds of British English. These apps also provide feedback on your pronunciation, which can help you identify areas for improvement.Additionally, consider using pronunciation apps such as Speech Ace or ELSA Speak, which are specifically designed to help learners improve their pronunciation skills. These apps offer targeted exercises for mastering vowel and consonant sounds, stress patterns, and intonation in British English. By using language learning apps, you can practice pronunciation in a fun and engaging way.7. Enroll in a Pronunciation CourseIf you are serious about improving your British English pronunciation, consider enrolling in a pronunciation course. Many language schools and online platforms offer courses specifically designed to help learners improve their pronunciation skills. These courses typically cover a range of topics, including phonetics, stress and intonation, and vowel and consonant sounds.A pronunciation course can provide you with structured guidance and feedback on your pronunciation. A qualified instructor can help you identify areas for improvement and offer personalized advice on how to enhance your pronunciation skills. Additionally, enrolling in a pronunciation course can provide you with opportunities to practice speaking with other learners and receive support from a community of language enthusiasts.8. Record YourselfA useful technique for improving your British English pronunciation is to record yourself speaking and listen to the playback. By recording yourself, you can identify areas where you may be mispronouncing sounds or words and work on correcting them. Pay attention to your intonation, stress patterns, and articulation of sounds as you listen to the recording.Practice recording yourself reading aloud from a book, newspaper, or script. Pay attention to your pronunciation, rhythm, and pace as you speak. After listening to the recording, make note of any areas where you may need to improve and practice those sounds or patterns accordingly.Additionally, consider using technology such as speech recognition software or pronunciation apps to provide feedback on your pronunciation. These tools can help you identify areas for improvement and track your progress over time. By recording yourself regularly and seeking feedback, you can improve your British English pronunciation effectively.9. Practice RegularlyImproving your British English pronunciation requires regular practice and dedication. Set aside time each day to practice pronunciation exercises, listen to native speakers, and engage with British English content. Consistency is key to mastering pronunciation skills, so make an effort to incorporate pronunciation practice into your daily routine.Practice speaking British English with native speakers whenever possible. Consider joining language exchange programs, attending conversation clubs, or hiring a tutor to practice speaking and receive feedback on your accent. By practicing regularly with native speakers, you can improve your pronunciation skills and gain confidence in speaking British English. Additionally, practice pronunciation drills and exercises daily to reinforce your learning. Focus on specific sounds, stress patterns, and intonation patterns to improve your accuracy and fluency in British English. By practicing regularly and consistently, you can make significant progress in mastering British English pronunciation.ConclusionLearning British English pronunciation can be a challenging but rewarding experience. By following the tips and strategies outlined in this article, you can improve your pronunciation skills and sound more natural in British English. Remember to focus on phonetics, stress, intonation, and vowel and consonant sounds, and practice regularly to see progress in your pronunciation.Whether you are a beginner or an advanced learner, mastering British English pronunciation is an achievable goal with dedication and practice. By immersing yourself in the language, using language learning apps, enrolling in a pronunciation course, and recording yourself regularly, you can improve your pronunciation skills effectively. With persistence and determination, you can confidently communicate in British English and enhance your language learning journey.。

英语句子发音标准领读

英语句子发音标准领读English Answer：Pronunciation is a crucial aspect of language learning, enabling clear and effective communication. To achieve standard English pronunciation, consider the following guidelines:1. IPA Mastery: Familiarize yourself with the International Phonetic Alphabet (IPA), a universal system that represents speech sounds accurately. It provides precise symbols for each English phoneme.2. Native Speaker Exposure: Immerse yourself in listening to native English speakers through podcasts, videos, and conversations. Pay attention to their pronunciation, intonation, and rhythm.3. Shadowing Practice: Repeat after native speakers, imitating their speech patterns and sounds. This helpsdevelop muscle memory and internalize accurate pronunciation.4. Tongue Twisters and Minimal Pairs: Practice tongue twisters and minimal pairs (words that differ only in a single sound) to improve articulation and discrimination of similar sounds.5. Use of Dictionaries: Refer to online or physical dictionaries with phonetic transcriptions to verify pronunciation and understand the correct sound of words.6. Feedback and Correction: Seek feedback from native speakers or language teachers to identify and correct any pronunciation errors. Regular practice and corrections will enhance your pronunciation skills.7. Patience and Consistency: Pronunciation improvement takes time and consistent effort. Practice regularly, avoid mimicking non-native accents, and strive for progress over perfection.中文回答：英语发音标准领读。

Frenchay言语困难评定

Frenchay言语困难评定简介本文档旨在介绍Frenchay言语困难评定，该评定是一种用于评估人们的语言能力和沟通困难程度的工具。

以下是完整版的评定内容。

评定内容1. 个人基本信息：评定开始时，记录被评估者的个人信息，包括姓名、性别、年龄等。

2. 外貌和面部肌肉：评定者观察被评估者的外貌和面部肌肉活动，并记录任何异常或受限的发现。

3. 口唇活动：评定者观察被评估者的口唇活动，包括闭合、张合、伸缩等，并记录任何异常或受限的发现。

4. 咀嚼和吞咽：评定者观察被评估者的咀嚼和吞咽动作，并记录任何异常或受限的发现。

5. 发音：被评估者被要求发出不同的声音和词语，评定者根据发音的准确程度进行评分。

6. 语言理解：被评估者被要求理解和回答一些简单的问题，评定者记录其理解能力和回答的准确程度。

7. 语言表达：被评估者被要求用语言表达一些简单的句子或表达意见，评定者记录其表达能力和流利程度。

8. 阅读和书写：被评估者被要求阅读一段简单的文字并回答相关问题，评定者记录其阅读和书写能力。

评分标准评定者根据被评估者的口服动作、发音准确度以及语言理解和表达能力等方面进行综合评分。

评估结果可参考以下评分标准：- 0分：完全无法进行相关操作或表达。

- 1分：能进行基本操作或表达，但存在严重困难。

- 2分：能进行大部分操作或表达，但存在一些困难。

- 3分：能进行所有操作或表达，且无明显困难。

结论根据评分结果，评定者可以得出被评估者的言语能力和沟通困难程度的结论。

评定报告应当详细描述评估结果，并为进一步帮助被评估者提供个性化的康复计划或治疗建议。

参考资料- Frenchay Dysarthria Assessment Manual- 美国康复医学会（American Academy of Physical Medicine and Rehabilitation）- 美国言语病理学和听力学会（American Speech-Language-Hearing Association）- 美国康复治疗师协会（American Occupational Therapy Association）- 国际言语-语言病理和治疗学会（International Society of Speech-Language Pathology and Therapy）。

英语口语中的变音现象及读音规则

英语口语中的变音现象及读音规则The phenomenon of sound change in spoken English is a fascinating aspect of language that can often perplex learners. This process, known as allophony, involves variations in the pronunciation of a single phoneme that do not change the meaning of a word. Understanding these subtle shifts is crucial for achieving a natural-sounding accent and can greatly aid in both comprehension and communication.In English, allophonic variations are influenced by several factors, including the phonetic environment, the position of stress within a word, and even the speed of speech. For instance, the phoneme /t/ can be pronounced as a quick, soft tap of the tongue against the roof of the mouth, known as a flap, when it occurs between two vowels, as in the word "water." This is markedly different from the more forceful, aspirated /t/ sound heard at the beginning of "top."Another example is the /l/ sound, which can be "clear" or "light" when it appears before a vowel, as in "light," and "dark" or "velarized" when it comes after a vowel or at the end of a word, as in "full." The dark /l/ is characterized by a raising of the back of the tongue towards the soft palate, giving it a deeper, more resonant quality.Vowel sounds, too, are subject to change. The schwa /ə/, an unstressed and neutral vowel sound, is a chameleon in English pronunciation. It appears in countless words, often replacing what might be a more pronounced vowel in a stressed syllable. For example, the first 'a' in "about" and the 'e' in "problem" are both pronounced as a schwa.Stress patterns play a significant role in pronunciation as well. In multi-syllable words, the stressed syllable will have a clearer, more defined vowel sound, while unstressed syllables often reduce to a schwa. This is evident in words like "photography," where the second syllable bears the stress and has a full vowel sound, while the other syllables are more muted.The rhythm of English, which is stress-timed, means that stressed syllables occur at regular intervals, with unstressed syllables filling the gaps, often with reduced vowels. This rhythm is essential to mastering the natural flow of the language.Linking and elision are also key to fluid English speech. Linking occurs when a word ending in a consonant is followed by a word beginning with a vowel, allowing for a smoother transition between words. Elision, on the other hand, involves the omission of sounds in rapid speech, such as the 't' in "Christmas" often being silent.In conclusion, the rules of English pronunciation are complex and require careful study and practice. By paying attention to the nuances of allophony, stress patterns, rhythm, linking, and elision, learners can improve their spoken English, making it more comprehensible and authentic. Mastery of these elements is not only beneficial for communication but also enriches one's understanding of the language's structure and beauty. The journey to fluency is indeed a challenging one, but with persistence and attention to detail, it is certainly achievable. 。

distinctive features语言学

distinctive features语言学"distinctive features"（显著特征）是语言学中一个重要的概念。

它源于约瑟夫·格林伯格（Joseph Greenberg）的研究，用于描述语言中不同音素之间的差异。

本文将从定义、分类和应用三个方面详细讨论distinctive features在语言学中的作用。

"distinctive features"是用来描述语言中音素（speech sounds）的特征的概念。

它的基本思想是，语言中的音素可以通过一系列的二元特征来定义。

这些特征可以是对口腔的位置、运动或声带的振动等的描述。

例如，/b/音和/p/音之间的一项显著特征是声带振动（有声和无声）。

通过将这些特征应用于语言中的不同音素，我们可以区分并描述不同的语音。

"distinctive features"可以被分类为二进制特征和层级特征。

二进制特征是指这些特征要么具有，要么不具有。

例如，有声（voiced）和无声（voiceless）就是一个二进制特征。

层级特征则表示这些特征可以在更细粒度的级别上进行划分。

例如，喉头调节（laryngeal configuration）可以分为声带状态（state of thevocal folds）和喉头调节类型（type of laryngeal adjustment）两个层级特征。

"distinctive features"在语言学中的应用非常广泛。

首先，它可以用于描述语言的音位（phonemes）系统。

通过分析一个语言中不同音素之间的"distinctive features"，我们可以了解到该语言中的音素如何进行区分和组织。

这对于学习和教授某一语言的语音非常重要。

"distinctive features"也可以用来研究语音变异（phonetic variation）和语音变体（phonetic variation）。

英语音标发音规律总结

英语音标发音规律总结English Pronunciation Rules Summary。

English pronunciation can be a tricky thing for non-native speakers to master. With its many irregularities and exceptions, it's no wonder that so many people struggle with it. However, there are some general rules and patterns that can help learners understand and improve their pronunciation. In this document, we will summarize some of the key pronunciation rules in English.1. Vowel Sounds。

English has 20 vowel sounds, which can be divided into short and long vowels. The key to understanding vowel pronunciation lies in recognizing the differences between these two categories. Short vowels are typically found in closed syllables, while long vowels are often found in open syllables or before a silent e. For example, the word "bit" contains a short i sound, while the word "bite" contains a long i sound.2. Consonant Sounds。

训练发音的准确性英语作文

训练发音的准确性英语作文Title: Enhancing Pronunciation Accuracy: A Multifaceted Approach。

1. Unleash the Power of Spontaneity。

Imagine a language lesson where the rhythm of speech is not a chore, but a dance. In the realm of English pronunciation, spontaneity is the catalyst for growth.Don't拘泥于 grammar rules, let your voice flow freely, asif you're chatting with a friend (ChatGPT, that's me!).2. Speak Like a Native, Listen Like a Pro。

To train your ears, immerse yourself in authentic English. Listen to podcasts, watch movies, and evenpractice with language learning apps that mimic real-life conversations (Just like I'm doing here, engaging with you). The more you hear, the better you'll sound (ChatGPT, always ready to help).3. Break It Down, Build It Up。

Pronunciation isn't about memorizing every word, but understanding the phonetics. Break down words into their component sounds, and practice saying them correctly (ChatGPT, teaching you the ABCs of pronunciation).4. Practice Makes Perfect。

英语学习中如何提高发音准确性和自信度

英语学习中如何提高发音准确性和自信度English-learning Tips: Improving Pronunciation Accuracy and ConfidenceIntroduction:English pronunciation plays a crucial role in effective communication. It is essential to have accurate pronunciation to ensure clarity and understanding. This article aims to provide practical tips and strategies to enhance both pronunciation accuracy and confidence in English language learners.1. Developing Phonemic Awareness:Phonemic awareness refers to the ability to hear and identify individual sounds (phonemes) in words. This skill is crucial for accurate pronunciation. Here are some activities to develop phonemic awareness:a. Practice minimal pairs: Identify and differentiate between similar sounds, such as "ship" and "sheep," "cat" and "cut."b. Use online resources: Utilize interactive phonetics websites or mobile applications to listen to and imitate various phonemes.2. Listening and Mimicking Native Speakers:Listening to native speakers and imitating their pronunciation is an effective method to improve accuracy and fluency. Here are some suggestions:a. Engage with authentic English materials: Watch movies, TV shows, or listen to podcasts featuring native speakers.b. Repeat aloud: After listening to native speakers, repeat the phrases, focusing on imitating their pronunciation.3. Utilizing Pronunciation Guides and Tools:There are various resources available to assist in improving pronunciation. Here are a few:a. Pronunciation dictionaries: Refer to online or physical dictionaries that provide pronunciation guides for each word.b. Speech recognition apps: Utilize apps that offer automatic feedback on pronunciation accuracy.c. English pronunciation websites: Explore websites that provide instructional audio and visual resources for practicing specific sounds or words.4. Engaging in Conversational Practice:Engaging in regular conversation practice is paramount for improving pronunciation and gaining confidence. Consider the following:a. Find conversation partners: Join language exchange programs or conversation groups to practice speaking with native English speakers.b. Record and evaluate yourself: Record your conversations and listen back to identify areas of improvement.c. Focus on stress and intonation: Pay attention to the stress and intonation patterns in sentences, as they can greatly impact the overall meaning and communication.5. Seeking Professional Guidance:Seeking guidance from a qualified English teacher or speech therapist can provide personalized feedback and targeted practice. Here are some options:a. Private lessons: Enroll in one-on-one sessions with an English tutor who specializes in pronunciation training.b. Pronunciation courses: Join pronunciation-focused courses offered by language centers or online platforms.c. Speech therapy: Consult a speech therapist who can identify and address specific pronunciation difficulties.Conclusion:Improving pronunciation accuracy and confidence in English is an ongoing journey that requires dedication and practice. By developing phonemic awareness, mimicking native speakers, utilizing pronunciation guides and tools, engaging in conversational practice, and seeking professional guidance, learners can enhance their pronunciation skills effectively. Remember, consistent effort and perseverance are key to achieving proficiency in English pronunciation.。

正确发音的方法

正确发音的方法Pronunciation is a crucial aspect of language learning. It plays a significant role in effective communication and understanding. Without proper pronunciation, a person may struggle to be understood or may find it challenging to comprehend others. Therefore, mastering the correct pronunciation is essential for language learners.发音是语言学习中至关重要的一部分。

它在有效沟通和理解中扮演着重要角色。

没有正确的发音，一个人可能会难以被理解，或者难以理解他人。

因此，掌握正确的发音对于语言学习者至关重要。

One of the most effective ways to improve pronunciation is through regular practice. By consistently practicing pronunciation exercises, learners can train their mouths and tongues to produce the correct sounds. This can help develop muscle memory, making it easier to pronounce words accurately in the future.最有效提高发音的方法之一是通过定期练习。

通过持续练习发音练习，学习者可以训练他们的口腔和舌头发出正确的声音。

这有助于培养肌肉记忆，使将来更容易准确发音。

Another useful technique for improving pronunciation is to listen actively to native speakers. By listening carefully to how words are pronounced by those who speak the language fluently, learners can mimic and replicate those sounds. This can help with understanding intonation, stress, and rhythm in speech.提高发音的另一个有用技巧是积极倾听母语者。

发音矫正演讲稿范文英语

Good morning/afternoon/evening. Today, I am here to talk about the importance of pronunciation correction. As we all know, language is a powerful tool that connects us and allows us to communicate effectively. However, poor pronunciation can hinder our ability to convey our thoughts and ideas clearly. Therefore, it is essential to work on our pronunciation to improve our communication skills. In this speech, Iwill discuss the reasons why pronunciation correction is important, the challenges we may face, and some practical tips to help you improve your pronunciation.Firstly, let's talk about why pronunciation correction is important. Effective communication is built on the foundation of clear and accurate pronunciation. When we speak with proper pronunciation, we make it easier for others to understand us, which can lead to better relationships, both personally and professionally. Moreover, a good command of pronunciation can help us avoid misunderstandings and misinterpretations, which can be crucial in various situations, such as business negotiations, academic discussions, or even in our daily lives.Now, let's explore some of the challenges we may face when trying to correct our pronunciation. One of the most common challenges is the lack of awareness. Many people are not aware of their mispronunciations, and this lack of awareness can hinder their progress. Another challenge is the lack of practice. Improving pronunciation requires consistent and dedicated practice, and without enough practice, our efforts may not yield the desired results. Lastly, we may encounter difficulties in acquiring new sounds, especially if they are foreign to our native language.To overcome these challenges, here are some practical tips to help you improve your pronunciation:1. Listen carefully: Pay attention to how native speakers pronounce words and phrases. You can do this by watching movies, listening to podcasts, or joining language exchange groups. By listening to native speakers, you will become more aware of the correct pronunciation and be able to mimic it.2. Practice regularly: Set aside time each day to practice your pronunciation. You can use language learning apps, workbooks, or even create your own exercises. Consistency is key to making progress.3. Record yourself: Recording your speech can help you identify your mispronunciations. By listening to yourself, you can pinpoint areas that need improvement and work on them accordingly.4. Work on your mouth: Pronunciation involves more than just the sound; it also involves the position of your mouth and tongue. Practice different mouth positions and tongue placements to master new sounds.5. Seek professional help: If you are struggling to improve your pronunciation, consider seeking help from a language tutor or a speech therapist. They can provide personalized guidance and support.6. Don't be afraid to make mistakes: Remember that making mistakes is a natural part of the learning process. Don't be discouraged by setbacks; instead, use them as opportunities to learn and grow.In conclusion, pronunciation correction is an essential aspect of language learning. By addressing our mispronunciations, we can enhance our communication skills, build better relationships, and avoid misunderstandings. Although it may seem challenging, with consistent practice and the right strategies, we can overcome our pronunciation obstacles and become more effective communicators.Thank you for your attention. I hope you find these tips helpful in your journey towards mastering pronunciation. Good luck!。

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PRONUNCIATION VARIATION SPEECH RECOGNITION WITHOUT NEW DICTIONARY CONSTRUCTION Supphanat Kanokphara, Virongrong Tesprasit, Rachod ThongprasirtInformation R&D Division,National Electronics and Computer Technology Center (NECTEC)112 Paholyothin Rd., Klong 1, Klong Luang, Pathumthani 12120 Thailand (supphanat_k, virong, rachod)@nectec.or.thABSTRACTGenerally, a speech recognition system uses a fixed set of pronunciations according to the dictionary for training and decoding. However, even a well-defined dictionary cannot be used to support all variations in human’s pronunciation. Besides, in order to cover all possible pronunciations, the size of the dictionary would be too large to implement. This paper presents efficient strategies for both training and decoding of a continuous speech recognition system: tree of knowledge-based pronunciation variations re-label training and state-level pronunciation variation model, respectively. These strategies can efficiently support the variations in pronunciation according to the rules without necessity to make pronunciation variation dictionary. The pronunciation variation training is modified from the re-label training to obtain the maximum likelihood pronunciation during training in order to reduce the error in an acoustic model. Although the database and rules used in this paper is Thai, this system can also be adapted to other languages easily as the variations are controlled by simple rules. The system shows better performance in the experiment.1.INTRODUCTIONHidden Markov Model (HMM) takes parts deeply in the field of speech technologies. This is because the properties of HMM which can normalize speech signal’s time variation and represent the speech signal statistically. In the HMM training process, the Baum-Welch method [1] is unavoidably used to optimize the acoustic model. In the training process, an acoustic model is built from speech data and transcriptions according to the given speech. By this way, the acoustic model should be accurate if the speech data are correctly transcribed. However, the accuracy of acoustic model is usually limited by the precision of the pronunciation in the dictionary. As a result, many researchers [2][3][4] tried to construct a dictionary to contain all possible pronunciation variations automatically. By doing this, the variations of pronunciation are also constrained by the size of dictionary and the corpus used to construct the dictionary.This variation problem becomes acute in the language having wide variation in speaking like Thai language. One of the problems of Thai language is there are too many pronunciations similar to each other such as /l/ and /r/. Therefore, in the real conversation, /l/ and /r/ can be pronounced incorrectly.Our approach is similar to [4]. The differences are (i) there is no pronunciation variation dictionary constructed, (ii) each Tree is applied to each word, not each phoneme, (iii) instead of yes-no question; the question is “What is the most probably phone for this phoneme?” while each node represents phoneme next to its parent. The best pronunciation is chosen from the last node when there is no next phoneme left in the word’s pronunciation.By training acoustic model with this strategy, the model becomes weaker for the variation of testing data if there is no appropriate variation in the dictionary. Linking all possible variation phonemes together and retraining allows the real pronunciation to be searched during the decoding process. This pronunciation variation model can give the real variant during decoding without dictionary construction. These combination strategies greatly reduce a lot of time-consumption because it can vary the pronunciation while training and testing.This paper is organized as follows. The next section describes all the Thai pronunciation variation rules used in this paper. Then, the training strategy is described in Sections 3. The experimental results and conclusion are described in Sections 4 and 5, respectively.2.PRONUNCIATION VARIATION RULES Table 1 [10] demonstrates all 76 phonemes used in this paper. “sp” and “sil” are short pause and silence symbols, respectively. A double character means long vowel such as /@@/ is a longer version of /@/. Some vowels are not included in Table 1 because they have fewer occurrences in Thai speech such as /ia/, /ua/, etc. A character with “^” symbol indicates the final consonant. A character combined with “h” is the aspirated version of that sound such as /kh/ is the Initialconsonants/k, kh, ng, c, ch, s, j, d, t, th, n, b, p, ph, f,m, r, l, w, h, z/Clusterconsonants/pr, pl, tr, kr, kl, kw, phr, phl, thr, khr, khl,khw, br, bl, fr, fl, dr/Finalconsonants/k^, ng^, j^, t^, n^, p^, m^, w^, z^,ch^, f^,l^, s^, jf^, ks/Vowels /a, aa, i, ii, v, vv, u, uu, e, ee, x, xx, o, oo,@, @@, q, qq, iia, vva, uua/Specialsymbolssil, spTable 1: Phonemes for Thai words in this paper.aspirated version of /k/. Character with /w/, /r/ and /l/ are called cluster /w/, /r/ and /l/, respectively (cluster is pronounced two phonemes together).There are 4 rules in this paper as follows:(a)“sp” insertionThai language does not have punctuation marker to pause within a sentence, a short pause can occur anywhere after syllable. As a result, the short pause is selectively inserted at the end of each syllable in each word. In Thai language, the end of the syllable can be either vowel or final consonant. Additionally, the beginning of the syllable must be an initial consonant or a cluster consonant.(b)/r/ sound ÅÆ /l/ sound (nonstandard pronunciation)/r/ sound is difficult to pronounce in the real speech. For convenience, sometimes /r/ sound is pronounced as /l/ sound. Contrarily, some over-accented Thai speakers would produce /l/ sound as /r/ sound. The phonemes following this rule are listed below.•/pr/ ÅÆ /pl/ ÅÆ /p/•/tr/ ÅÆ /t/•/kr/ ÅÆ /kl/ ÅÆ /k/•/phr/ ÅÆ /phl/ ÅÆ /ph/•/thr/ ÅÆ /thl/ ÅÆ /th/•/khr/ ÅÆ /khl/ ÅÆ /kh/•/br/ ÅÆ /bl/ ÅÆ /b/•/fr/ ÅÆ /fl/ ÅÆ /f/•/dr/ ÅÆ /d/•/r/ ÅÆ /l/(c)Loan word error [11]Some pronunciations of loan words are hard to pronounce in Thai. Some speakers pronounce those words in English accent while some pronounce in Thai accent. The phonemes following this rule are listed below.•/s/ ÅÆ /ch/•/l^/ ÅÆ /n^/ ÅÆ /w^/•/s^/ ÅÆ /t^/•/f^/ ÅÆ /p^/•/ch^/ ÅÆ /t^/•/t/ ÅÆ /th/•/p/ ÅÆ /ph/•/k/ ÅÆ /kh/(d)“Short vowel” ÅÆ “long vowel”In conversation, a fast speaking rate would shorten some Thai vowels. In the same way, a slow speaking rate would lengthen a vowel. The phonemes following this rule are listed below.•/i/ ÅÆ /ii/•/e/ ÅÆ /ee/•/a/ ÅÆ /aa/•/@/ ÅÆ /@@/•/x/ ÅÆ /xx/3.PRONUNCIATION VARIATION MODEL In order to achieve the high accuracy acoustic model, speech data should be correctly marked. Nevertheless, for many reasons, the transcriptions are not perfectly marked. Traditionally, transcriptions are generated from automatic segmentation and rechecked by human. However, in a large database, manual-checking process is time-consuming and usually ignored. The re-label training strategy [6] is designed to update transcriptions during the training so that the high accuracy model can be obtained without manual process. This training strategy increases model accuracy by correcting the transcription according to the pronunciation list in the dictionary.In this paper, this system is extended to cover large phoneme variations unable to be listed in the dictionary. As mention before, the real speech is varied according to many factors, such as noise, speaking style, etc. These variations are too large to be supported by any dictionary. Fig. 1 shows the tree-based pronunciation variation re-label training flowchart. The system starts from word transcriptions, dictionary, initial acoustic model and speech database as inputs. This acoustic model is trained from initial phoneme transcriptions and speech database with the re-estimation algorithm. The tree-based pronunciation variation then generates phoneme transcription according to the inputs and pronunciation variation rules. This phoneme transcription and speech data are then the inputs for re-estimation. After that, the re-estimation process updates the acoustic model to be the input for tree-based pronunciation variation. This process is continued until the log probability of update model is less than the last one.3.1.Tree-based pronunciation variationThis section clarifies the tree-based pronunciation variation, one of the processes mentioned above. This process uses word transcriptions, dictionary, acoustic model and speech database as inputs. The process starts from placing the first phoneme of each word of each transcription at the root node of the variation decision tree. Then, as the tree path goes down, the phoneme in the question is the phoneme next to the last phoneme from its parent node. This iteration continues until there is no phoneme next to the phoneme in the question. For example, the word “rak^” (=love) in Fig. 2, the question at the root node is “What is the possible variation of /r/ following by /a/ of the word?”. The candidate for this question is /l/ according to the rule (b) in Section 2. The second level node is associated with the question “What is the possible variation of /a/ preceding by Figure 1: Tree-based pronunciation variation re-label trainingprocess.Converge?Word transcriptions, dictionary andFinalNoYes/l/ and following by /k/?”. The possible variant is /aa/. The question is continued until there is no next phoneme left. As this is a three-phoneme word, there are only four tree levels in this example. The darker line shows the best tree path and the leaf node is the best pronunciation for this word which is “lak^” as shown in Fig. 2. The question of child node depends on the answer from the parent node because some rules can be applied only at the specific preceding and following phonemes such as “sp” can be inserted after vowel if the following phoneme is an initial or cluster consonants, not a final consonant.The question at each node is answered by using Viterbi algorithm giving the acoustic model. By this way, the best pronunciation of each word in each transcription is chosen according to the speech database in the maximum likelihood sense. In common sense, searching by using tree eliminates unnecessary path in the process comparing with searching on every possible pronunciation in the pronunciation lists. In practice, this reduces the number of possible candidates without any degradation in performance.3.2.Pronunciation variation modelBy using pronunciation variation training, the model is trained from the real transcriptions. For example, the word “rak” can be trained as the word “lak” if the speaker really read “lak” in the recording process. With this training strategy, the model “l” or “r” becomes the real “l” or “r”, respectively. Consequence, these models cannot well recognize “l” pronounced as “r” or “r” pronounced as “l” in the testing data. Of course, it is impossible to construct such a gigantic-size dictionary covering all possible pronunciations of every word. The tree-based pronunciation variation also cannot be used at this point because there is no given word in the decoding process. The problem can be solved by allowing the alternative path of pronunciation in each model during recognition. This can be done by tying the start and end states of the all models in the same variation group according to the rules presented in Section 2. The transitions from the first state to each individual sub model are all the same in order to allow the fair pronunciation variation. These prototype models are all retrained to obtain maximum likelihood models. With this model, the best matching path can be obtained even when no variation presents in the dictionary. For example, phonemes /l/ and /r/ can be varied according to the rule in Section 2. The prototype of pronunciation variation model /l/ or /r/ is shown in Fig. 3. The transitions to /r/ and /l/ are equally divided to be 0.5.4.EXPERIMENTHTK Toolkit [5] is used as the base system for this experiment. The experiment procedure starts from data preparation, wave to MFCC conversion, 5-state 1-mixture left-right model prototype building, transcription labeling and dictionary construction (in HTK format), model training, and testing finally.There are two points concerned in this experiment, i.e. effect of initial phoneme transcriptions and training strategy. There are three types of initial phoneme transcriptions: the transcriptions generated automatically by using Thai Grapheme-to-Phoneme (G2P) developed by NECTEC [7] (I), the transcriptions edited by expert labelers (II), and the transcriptions generated from re-label training processes (III). Training strategies are: training without re-label re-estimation (IV), training with re-label re-estimation (V), training with pronunciation variation (VI).A back-off bi-gram language model is constructed as the speech data in this experiment is continuous and Viterbi algorithm is applied for speech recognition process.4.1.DatabaseIn 3,097 words database, 1,246 continuous read speech utterances are used as a training set and 140 continuous read speech utterances having less error in language model are selected as a testing set. Each utterance has approximately 10 words. As this experiment aims at improving of acoustic model, we designed the experiment to have less effect from language model error, such as out-of-vocabulary problem, insufficient number of tri-gram for training, etc. This can be done by selecting the most-occurrence-words sentences as a test set. The algorithm of selecting test sentences is somewhat similar to [8]. A female professional speaker is set to record all speech utterances in order to avoid any error occurring from speaker’s specific characteristic.The manual phoneme transcriptions are generated from G2P and edited by our expert labelers. The transcriptions were examined by using Wavesurfer 1.0.4 [9]. There are only 2 expert labelers for the correction process in order to preserve the consistency. Complicated points in transcription and boundary alignment are discussed and adopted during the process.The language model is constructed from 1,246 sentences according to the utterances. Back-off bi-gram’s perplexity is 73.68 and entropy is 6.20. Dictionary is generated from G2P.r a k^r+aFigure 2: Variation decision tree of the word "rak^". Figure 3: Prototype for "l" or "r" pronunciation variationmodel.Speech utterances (16 kHz sampling frequency with 16 bits quantization) are parameterized into 12 dimensional vectors, energy, and their delta and acceleration (39 length front-end parameters).4.2. ResultsThere are many training types in this experiment according to initial phoneme transcriptions and training strategies. As mentioned above, three types of initial phoneme transcriptions are listed as I, II and III, and three types of training strategy are as IV, V and VI. For example, in Table 2, I + IV means training without re-label re-estimation, and the system is initialized by automatic generated phoneme transcriptions. Training log probability tells us how the acoustic model is close to the training data. Percentage of accuracy and correction declares how the recognition result matches the testing data. Therefore, observing all three values gives both the effect of the system for training and testing data.In Table 2, training by using manual phoneme transcriptions is the best method. This shows that phoneme transcriptions edited by our labeller are good in quality. It also shows that the phoneme transcriptions generated from our re-label training system give better result than the one from G2P. Moreover, the accuracy is only 0.59% less than the system initialized by manual phoneme transcriptions.Table 3 shows the results from training with re-label re-estimation. The systems trained by manual and re-label phoneme transcription (II and III) are the same as in Table 2. This is because they are already satified and need no re-label in the maximum likelihood sense. The effect of re-label training can be seen from the training initialized by G2P phoneme transcriptions. The accuracy is increased by 3.31%.The result of pronunciation variation approach to thesystem is demonstrated in Table 4. The result from this Table shows the superior result to the Table 2 and 3. The higher percentage correction of III than II also illustrates that the efficiency of transcription generated automatically can reach the level of the manual one if our pronunciation variation processes are applied.5. CONCLUSIONIn this paper, we have proposed an efficient way ofpronunciation variation approach to the speech recognition. Tree-base pronunciation variation and pronunciation variation model are used for training and decoding, respectively. The merits of these techniques are: (1) they can be implemented easily and can be applied to any languages. (2) Tree base search is faster comparing with the direct pronunciation search. (3) The system results in better performance.There is no tonal experiment performed in this paper. The tonal experiment will be done in the future work.6. REFERENCES[1]L. R. Rabiner, “A Tutorial on Hidden Markov Models andSelected Appplications in Speech Recognition'', Proc. IEEE, 77 (2): 257-286, 1989.[2] H. Nakajima, Y. Sagisaka, H. Yamamoto, “PronunciationVariants Description using Recognition error Modeling with Phonetic Derivation Hypotheses'', Proc. ICSLP2000, (3): 1093-1096, 2000.[3] J. Jeon, S. Cha, M. Chung, J. Park, K. Hwang, “AutomaticGeneration of Korean Pronunciation Variants by Multistage Applications of Phonological Rules”, Proc. ICSLP 98.[4] M. Riley, W. Byrne, M. Finke, S. Khudanpur, A. Ljolje, J.McDonough, H. Nock, M. Saraclar, C. Wooters, G. Zavaliagkos, “Stochastic pronunciation modeling from hand-labelled phonetic corpora”, Speech Communication 29: 209-224 (1999).[5] Young, S., Jansen, J., Odell, J., Ollasen, D., Woodland, P.,1995. The HTK Book, Version 3.0, Entropic Cambridge Research Laboratory, Cambridge, England.[6] P. Tarsaku, S. Kanokphara, “A Study of HMM-basedautomatic segmentations for Thai Continuous Speech Recognition System”, Proc. SNLP2002.[7] P. Tarsaku, V. Sornlertlamvanich, R. Thongprasirt, “ThaiGrapheme-to-Phoneme using Probabilistic GLR Parser”, Proc. Eurospeech , (2): 1057-1060, 2001.[8] J. Shen, H. Wang, R. Lyu and L. Lee, “AutomaticSelection of Phonetically Distributed Sentence sets for Speaker Adaptation with Application to Large Vocabulary Mandarin Speech Recognition”, Computer Speech and Language , (13): 79-98, 1999.[9] K. Sjölander, J. Beskow, “Wavesurfer – An Open SourceSpeech Tool”, Proc. ICSLP , (4):, 464 - 467, 2000.[10] Khanitthanan, W., Phasa lae Phasasart, ThammasatUniversity Press, 1990. (in Thai)[11] Sirivisoot, S., Variation of Final (l) in English Loanwordsin Thai According to Style and Educational Background, Master Thesis, Department of Linguistics, Chulalongkorn University, 1994. (in Thai) Training type Training log probability % Correction % Accuracy I + VI -58.38 77.66 72.46 II + VI -57.60 79.42 74.11 III + VI-57.6880.4675.42Table 4: Training with pronunciation variation. Training type Training log probability % Correction % Accuracy I + V -58.73 77.87 72.77 II + V -58.63 78.52 73.63 III + V-58.7178.1172.91Table 3: Training with re-label re-estimation. Training type Training log probability % Correction % Accuracy I + IV -59.58 70.36 67.87 II + IV -58.63 78.52 73.63 III + IV-58.7374.0171.56Table 2: Training without re-label re-estimation.。