清华大学电子工程系

Acoustical Model
p(Y | W ) = ∑ p(Y | M ){∑ p( M | L)( ∑ p( L | Q ) p(Q | W ))}
M L Q
where L is number logical phones; M is number of shared physical models.
Basic Modeling Framework
Optimal word sequence W * :
W * = arg max p (W | Y )
W W
= arg max p (Y | W ) p (W )
Acoustic Modeling
p (Y | W ) = p (Q | W ) =
∑
Q
dk ∑m k =1 k
M
Exponential 0.05
d ' k = d k / speed
15 0 0 5 10 15
7
20
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Basic Modeling Framework
Beam Search Algorithms
Path-pruning:
Qmax = max {Q ( w, τ , t )} and Q ( w , τ , t ) < f * Q max
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The State of the Art
Gaussian mixtures for compensating intra-speaker and inter-speaker variability.
Ms b j ( ot ) = ∏ ∑ c j s m N ( o s t ; µ j s m , Σ j s m ) s =1 m =1
p λ (Y | M W ) p (W ) ∑ p λ (Y | M Wˆ ) p (Wˆ )
ˆ W
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The State of the Art
Linear discriminant analysis (LDA), confusion data ana discriminant analysis (HAD) for improving the discrimination of model.
p (Y | Q ) =
∑
X
p ( X ,Y | Q )
T
p ( X , Y | Q ) = a x ( 0 ), x ( 1 ) ∏ t =1 b x ( t ) ( y t ) a x ( t ), x ( t + 1 )
where X = x(0)Lx(T ) is a state sequence through the composite model. Model parameters can be efficiently estimated from a corpus of training utterances using EM algorithm.
P(O / H 0 ) P(O / H1 )
P(Wi / O) =
P(O, Wi ) P(O / Wi )P(Wi ) = P(O) P(O)
A Posterior probability can be approximated by:
P (O / W ) P (O / H 0 ) ≅ P (O ) P (O / H 1 )
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The State of the Art
Speaker Adaptation
Adaptation seeks to modify the models to make them a better fit to the speech. Two main approaches are used Model parameters can be treated as random variables and estimated using traditional Bayesian MAP techniques. Maximum likelihood linear regression (MLLR). MLLR seeks to find an affine transform for Gaussian means which maximizes the likelihood of the adaptation data. Fast adaptation Eigenvoice (用一组特定人模型来快速自适应新的说话人 模型 ) Extended Maximum a Posteriori, EMAP (主要考虑模型之 主要考虑模型之 间的空间相关性) 间的空间相关性
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The State of the Art
Robust Speech Recognition
Microphone array Normalization Channel normalization by cepstral mean subtraction RASTA (Relative Spectra) Vocal tract length normalization for speaker variations PMC (Parallel Model Combination) Speech enhancement technology Speech enhancement + PMC At present no good effective methods
动态规划在语音识 别中的应用
1960 语音产生的声学 理论 1970 LPC 在 语 音 识 别 中的应用 HMM以及相应技 以及相应技 术在语音识别中 的应用
DTW算法的出现 算法的出现 1980
1990 非特定人大词汇 量连续语音识别 算法逐步成熟
语音识别技术发展历史中的重要事件
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State-specific-based confusion classes are collected. The state-specific linear discriminant transformation is used. The discrimination of the acoustic models are improved by using discriminant analysis.
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Basic Modeling Framework
Language Modeling N-Gram
p(W ) = ∏ k =1 p ( wk | wk −1 , wk −2 ,L, wk −n +1 )
K
Decoding
Compiling a network of all vocabulary words in parallel with a loop. Each word is represented by the sub-network of HMM phone phone models. Exploit sharing and pruning to limit the number of active hypotheses. Multiple pass with a lattice of word sequence hypotheses. Different models are used in different pass.
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Basic Modeling Framework
The base phones Q consist of composite HMM formed by concatenating all of constituent based phones, the acoustic likelihood is given by
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The State of the Art
Confidence Measure (CM)
Information Source Likelihood Duration Likelihood Ratio:
LR(O, H 0 , H1 ) =
Confidence Measure (CM) Effects Posterior probability:
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Basic Modeling Framework
Duration-Based HMM or Segmental HMM
Probability of spectrum and durations can be expressed as:
P ( O , τ ) = P ( O ) × P (τ )
( w ,τ ,t )
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The State of the Art
Phonological Modeling
Co-articulation Tri-phone and bi-phone Decision tree clustering for shared physical model and shared state by using phonetics knowledge Using soft-tying. Group each state with its one or two nearest neighbors and polls all of their Gaussian.
Dynamic beam pruning method using Q ( w , τ , t ) Beam width be fixed to constant with N-best paths Path Merging: When two or more paths meet, it may be possible to merge them. Strategy: Merges may occur when the respective history equivalence classifications match.