通信电子工程类论文外文翻译、中英文翻译、外文文献翻译

The effect of speech recognition on working postures, productivity and the perception of user friendlinessElsbeth M. de Korte Piet van LingenAbstractA comparative, experimental study with repeated measures has been conducted to evaluate the effect of the use of speech recognition on working postures, productivity and the perception of user friendliness. Fifteen subjects performed a standardised task, first with keyboard and mouse and, after a six week training period, with speech recognition. The use of speech recognition leads to improved postures of wrist, forearm, upper arm and shoulder and improvement of neck movements when compared to the use of keyboard and mouse. Although the observation method was basic, this study provides insight into the potential benefits speech recognition has for posture. However, productivity decreased for most subjects and speech recognition appears to be usable for specific tasks only. From the perspective of productivity and the perception of user friendliness further development of speech recognition software is necessary. Up to now, speech recognition seems especially beneficial for people with WMSD complaints.1. IntroductionWork-related musculoskeletal disorders (WMSD) are common among VDU users (Office Ergonomics Re-search Committee, 1998; Gerr et al., 2002; Blatter and Bongers, 1999; Health Council of The Netherlands,2000; Otten et al., 1998). Visual display unit (VDU) users are at risk of developing neck, shoulder, wrist and hand complaints. The duration of VDU work, as well as awkward postures of neck, shoulders, wrists and hands are important risk factors. Also, the lack of arm support may lead to complaints of neck, shoulder, arm and hand (Punnett and Berqvist, 1997; Hales and Bernard, 1996; Marcus et al, 2002; Blatter and Bongers, 2002; Otten etal., 1998; Tittiranonda et al., 1999; Office Ergonomics Research Committee, 1998).With the arrival of a new generation of continuous speech recognition software, speech recognition becomes interesting as a new kind of input device. It can replace both mouse and keyboard. Furthermore, because speech recognition software can be operated without using the hands, it enables users to move freely at the workplace. Avoiding awkward postures becomes a possibility. Therefore, it is interesting to explore whether speech recognition can reduce one of the WMSD risk factors, that is awkward working postures, and whether it might play a role in prevention. However, research on this topic is limited.The relation between speech recognition and both the perception of user friendliness and productivity has been studied previously (Baber and Noyes, 1996; Baber et al.,1996; Ponsioen, 1999; Bekker et al., 1995; Noyes and Frankish, 1994). These studies have shown that the quality of recognition of speech is dependent on several factors, for example the voice of the user, which is influenced by emotions, stress, cold or fatigue. Other factors found to be of importance arebackground noise and amount of training to use speech recognition. When recognition quality decreases, the number of errors increases, which influences the productivity of the user negatively. Correcting errors in the right way is very important: the system needs to be trained constantly by the user to retain quality and to improve the system. Because productivity and user friendliness determine the actual purchase and use of speech recognition software to a large extent, it was important to involve these factors in this study.This study was designed to evaluate the effect of the use of speech recognition on working postures, productivity and user friendliness in comparison with the use of the traditional keyboard and mouse during VDU work.2. Subjects and methods2.1 Experimental designIn a comparative, experimental study with repeated measures, two kinds of input devices were tested. The traditional combination of keyboard and mouse was compared to speech recognition in a pre- and post-test, respectively. After the pre-test, the subjects received training in using speech recognition by a company specialised in speech recognition training. During 6 weeks all subjects used speech recognition (Dragon Naturally Speaking Dutch 3.6, which corre-sponds to the English 3.6 version) in their daily work. After 6 weeks, the post-test took place. Working postures and productivity were measured and user friendliness was assessed.At their own workstation, the subjects performed a standardised computer task which consisted of two subtasks. The first subtask consisted of making and sending e-mails in Microsoft Outlook for 5min, word processing and text editing in Microsoft Word for 9min, and changing between applications (Microsoft Excel, Power-point and Internet Explorer) for 1min. The total duration of this subtask was 15 min. During this subtask working postures were recorded on videotape. In the pre-test the subjects used keyboard and mouse. In the post-test they were allowed to use keyboard and mouse when they got stuck; it was not realistic to ask them to perform the first subtask with speech recognition only.The second subtask consisted of copying a text without editing. This task was performed for a maximum of 10min. The subjects were instructed to work at their own pace. No instructions were given on correcting errors. The second subtask was used to measure productivity. Consequently, the subjects were restricted to speech recognition in the post-test.2.2. SubjectsFifteen subjects, 9 men and 6 women, participated in the study. They all worked at the Dutch Ministry of Social Affairs. The subjects were experienced VDU workers and at least 50% of their daily work consisted of VDU work. They were all non-touch typists. Eight subjects suffered from WMSD. Seven subjects were free of injury. Their age varied between 25 and 55 years (Mean 42.5, SD 8.8). Before the experiment, participants gave informed consent.2.3. Measuring methodsWorking postures were recorded on videotape for the total duration of the first subtask (15 min). The camera was placed at the dominant side of the subject (Figs. 1).With The Observer (Noldus) the images of the first condition were analysed for duration of postures (a percentage of total time) and frequency of changing postures (number of changes per minute). The observedFig. 1. Schematic drawing of camera placement, rear view.Table 1Analysed postures (dominant side of the body)Body region VariablesNeck Flexion: <0°; 0–25°; >25°Rotation: yes/noShoulder Elevation (lifting): yes/noUpper arm Flexion (sagittal plane): 0–20°; >20°Forearm Pronation/ supination/ neutralArm support: on work surface/ on armrests/ no supportWrist Flexion 0–30°/ flexion>30°/ extension 0–30°/extension>30°Radial deviation/ ulnar deviation/ no deviationpostures were adopted from The Guidelines for Physical Load, developed by TNO Work & Employment (van der Grinten, 1999). The dependent variables are shown in Table 1. Differences in working postures were tested with a T-test for repeated measures. Significance level (a) was set at 5% (two-sided).Productivity measurements were taken from the second subtask. With The Observer (Noldus) the videotape was analysed for number of errors per minute and time spent on correcting errors (percentage of total time). Furthermore, the text was analysed for the number of words per minute entered correctly. Differences in productivity were tested with a T-test for repeated measures. Finally, perceived productivity was assessed by a question: Have you done more, less or the same amount of work since you started using speech recognition? Answers to this question are presented as frequencies.The perception of user friendliness of speech recognition was assessed with a questionnaire. This questionnaire was aimed at the 6 weeks the subjects were using speech recognition in their daily work. It was presented after the post-test. The questions inquired about how difficult it was to learn to use speech recognition, how long it took to learn it and preference for input device(keyboard, mouse or speech recognition). Furthermore, subjects were asked to name computer tasks they were suited to perform with speech recognition and tasks they were not. Finally, subjects were asked about how satisfied they were with using speech recognition. Data from the questionnaire are presented as frequencies. Two times, meetings were organised to exchange experiences. The first meeting was organised in the third week of the training period in order to share the first experiences with speech recognition software. The second meeting was organised after the experiments took place in order to evaluate the use of speech recognition software and to exchange views. Information from these meetings is also reported.3. Results3.1. Working posturesThe working posture results (Fig. 3a–h) show, with the use of speech recognition compared to keyboard and mouse, significantly less neck flexion, less pronation of the forearm and less ulnar deviation of the wrist. With the use of speech recognition arm support was used more often compared to the use of keyboard and mouse.As can be seen from Fig. 4, with the use of speech recognition, significantly less changes were found in neck flexion postures, neck rotation postures and arm support compared to the use of keyboard and mouse. When speech recognition was used, more changes were found in flexion postures of the upper arm.3.2. ProductivityThe number of words correctly entered per minute was significantly larger with the use of keyboard and mouse (mean 22.7, SD 6.7) compared to the use of speech recognition (mean 17.5, SD 8.8). Although no significant difference in the frequency of errors was found, the time spent on correcting errors was significantly longer with the use of speech recognition. With the use of keyboard and mouse 4.2% of the time is spent on correcting errors in contrast with 52.2% with the use of speech recognition. For productivity measurements, a large variation was found between the subjects. Nine subjects reported that they were as productive with using speech recognition as they were with using keyboard and mouse, 5 subjects were less productive and one subject was more productive.3.3. Perception of user friendlinessAs can be seen from Table 2, learning to use speech recognition software was …pretty diffi cult‟ according to six subjects, …pretty easy‟ according to 8 subjects and‘very easy‟ according to 1 subject. Nobody reported that the use of sp eech recognition software was …very diffi cult‟. The amount of time the subjects spent to learn to use speech recognition was described as …pretty much‟ by 8 subjects and …not so much‟ by 7 subjects.Nobody spent …very much‟ time. The majority of the subjects with WMSD complaints was satisfied with the use of speech recognition, in contrast with the injury free subjects. Subjects with WMSD complaints also preferred speech recognition as input device more often than the subjects who were injury free.Computer tasks that were reported to be suitable to be performed with speech recognition were Word-processing, making note‟s, making long E-mails, editing text, making lists and changing between applications. Tasks that were reported to be unsuitable to be performed with speechrecognition were working in spreadsheets such as Excel, Word-processing in a foreign language, making short E-mails, making tables and figures, correcting text, working with statistical applications such as SPSS and dealing with a calendar.From the meetings it appeared that speech recognition was appreciated as a complementary input device because it provides a means for users to vary their method of interacting with the computer. For the subjects who suffer from WMSD complaints speech recognition enables them to remain working. One important negative consequence of the use of speech recognition was that five subjects reported to have sore throats.Fig. 3a–h. Duration of postures in the first subtask using keyboard and mouse (KM) or speech recognition (SR) (*po0:05; **po0:01).Fig. 4. Frequency of posture changes in the first subtask (*po0:05).Table 2Perception of user friendliness of speech recognition outcomes related to gender, age and WMSDSubject Gender Age WMSD Difficulty to learn: Time to learn: Satisfied with Prefered input (years) Very easy Very much speech devicePretty easy Pretty much recognition KeyboardPretty difficult Not so much MouseVery difficult SpeechRecognition1 Female 30 No Pretty easy Not so much No Keyboard2 Male 25 No Pretty easy Pretty much No Keyboard3 Male 50 No Very easy Not so much Yes Speech recognition4 Male 34 No Pretty difficult Not so much No Keyboard5 Male 35 No Pretty easy Pretty much No Keyboard6 Male 44 No Pretty difficult Not so much No Keyboard7 Male 55 No Pretty difficult Pretty much Yes Speech recognition8 Female 44 Yes Pretty easy Pretty much Yes Speech recognition9 Female 42 Yes Pretty easy Not so much Yes Speech recognition10 Female 49 Yes Pretty difficult Pretty much Yes Speech recognition11 Female 42 Yes Pretty difficult Pretty much Yes Speech recognition12 Female 54 Yes Pretty easy Pretty much Yes Missing13 Male 40 Yes Pretty easy Pretty much No Keyboard14 Male 41 Yes Pretty difficult Not so much Yes Mouse15 Male 52 Yes Pretty easy Not so much No Keyboard4. Discussion4.1. Working posturesThe use of speech recognition software leads to better postures of wrist and forearm: both are held in neutral position more often. Since the wrist is more neutral when speech recognition is used, it may contribute to a reduction of WMSD risk factors. Sluiter et al. (2001),Punnett and Berqvist (1997) and Hagberg et al. (1995) found that working with wrists in a non-neutral posture is a risk factor for developing WMSD, although Marcuset al. (2002) found no evidence for a relationship between ulnar deviation and WMSD. Werner et al.(1997) found effects of pronation/ supination on carpal tunnel pressure. To what extent the decrease in pronation of the forearm contributes to a reduction of WMSD risk factors is not known.Also, upper arm and shoulder postures are improved if speech recognition is used, because arm supports are used more often. A decrease in changes between the different supported arm postures was found. It is not clear if that is desirable or not, although increases in postural fixity insemi-static tasks is generally not viewed as a positive situation. In this study, neck postures improved by the use of speech recognition software. Furthermore, when keyboard and mouse were used, neck postures were changed more often (8.2 times/min) compared to the use of speech recognition software (2.6 times/min) Looking at the keyboard less often may be an explanation for these findings, because it is not as much a necessity for non-touch typists when using speech recognition software. The increase in postural changes may lead to less static working postures. Since static working postures are a risk factor for developing WMSD complaints (Health Council of The Netherlands, 2000; Sluiter et al., 2001; Punnett and Berqvist, 1997; Hagberg et al., 1995), these findings may suggest that, from the perspective of prevention of static neck postures, it is preferable to use keyboard and mouse, as long as extreme end range positions are avoided. On the other hand, it could be argued that an increase in postural changes of the neck leads to repetitive movements, also a risk factor for developing WMSD complaints (Health Council of The Netherlands, 2000; Sluiter et al., 2001; Punnett and Berqvist, 1997; Hagberg et al., 1995). Kilbom (2000) defines repetitious movements as movements that occur more than 2 times/min, although this does not refer to neck movements. The long –term effects of changes in postures of the neck are not fully understood, yet. All in all, whether a decrease in neck posture changes from 8.2 to 2.6 times/min is an improvement remains unclear.In this study improved postures of neck, upper arm, forearm and shoulder were found, although the number of subjects was restricted due to financial and organizational limitations. The power of this study is therefore relatively low. Significance level (type I or a error), however, was set at the conventional level of 5% (two-sided). Differences and changes in this study that are‘signifi cant‟, will therefore all have a probability of 5%or less to be the result of chance or sampling error. If a difference or a change is not significant, however, this may largely be due to low power. This study is not well equipped to …prove‟ that a particular change from pre- to post-test does not exist in reality. Furthermore, the observations were imprecise in some cases due to the use of only one camera and large angular intervals. Also, there were large variations in the extent to which the subjects used both keyboard and mouse along with speech recognition in the first subtask, in which they were asked to use speech recognition as much as possible. All the same, the results provide useful insight into the potential benefits speech recognition has for posture.4.2. ProductivityIn Word processing, the mean number of words entered correctly per minute is lower with the use of speech recognition compared to the use of keyboard and mouse. However, when they were using speech recognition, the subjects spent half the time on correcting errors, compared to only 4% of the time when using keyboard and mouse. This indicates that there may be left much room for improvements in productivity of speech recognition. Productivity would improve substantially, if the number of errors and correcting time decreased. The fact that some of the subjects reached a higher number of words entered correctly per minute supports the idea that there is room for improvement. The pre- and post-test were not presented at the same moment of the day. Thus, there was not accounted for fatigue effects caused by work activities.The results of this study are comparable to those of Bekker et al. (1995). They compared mouse use with the use of speech recognition and found higher error rates when speech recognition was used. Nevertheless, speech recognition has developed considerably in quality of recognition sincethe study of Bekker et al. (1995) was performed.The results on productivity might have been influenced by the version of the speech recognition software. The subjects were not able to use the most recent version of the speech recognition software because it did not match in the other office applications used at the Dutch Ministry of Social Affairs. If the latest version of the speech recognition software was used, the productivity results might have been better. Also, the number of hours the subjects spent on training with speech recognition software may have influenced the productivity results. Almost none of the subjects completed the planned 40h of training, often as a result of deadlines and rush jobs. Also, large differences were found in the number of hours spent learning speech recognition. This could have led to large differences in skill. If the subjects had completed the 40h of training, the productivity results might have been better.The planned 40h of training with speech recognition software during daily work appeared to be a great demand on the productivity of the organisation as a whole. Nonetheless, for reintegrating employees with WMSD complaints, speech recognition could be the only option for being able to work at all with a computer.4.3. Perception of user friendlinessA fairly large part of the subjects (6 subjects) reported hat learning to use speech recognition software was‘pretty difficult ‟. In addition, it took quite some time to get speech recognition under control. Nevertheless, 6 subjects preferred speech recognition to keyboard or mouse, the majority of these subjects suffered from WMSD complaints. Unfortunately, there were no questions on the preferred combinations of input devices. Since speech recognition appears to be usable or specific tasks only and is usable along with other input devices, such as keyboard and mouse, its value might be a good complement to existing input devices.Five subjects reported a sore throat after using speech recognition. Further research is necessary to determine the effect of speech recognition on complaints with voice or throat. A few articles go into the voice demands associated with extended or frequent use of speech recognition, which can be high and may place users at risk for vocal difficulties, like muscle tension dysphonia.Kambeyanda et al., 1997; Olson et al., 2004; Haxer et al., 2001; Williams, 2003; Juul-Kristensen et al., 2004). Except the article of Juul-Kristensen et al. (2004) which is a comparative experimental study, these are all case reports. A noteworthy finding are the changes in speech patterns with the use of speech recognition. For example, Olson et al. (2004) reported that all cases had normal voice when using everyday speech, but speaking into the computer resulted in the rapid onset of a periodicity, strain, and a decrease in fundamental frequency. Changes in speech patterns were also found in EMG measurements of voice related muscles with the use of speech recognition (Juul-Kristensen et al., 2004). However, further studies are needed to investigate the findings in these articles. In the meantime, it is recommended that users become informed about the unnatural speech patterns used with speech recognition, learn to use good vocal hygiene, such as performing warm-up and cool-down voice exercises and use alternate methods of input along with the speech recognition product (Kambeyanda et al., 1997).Although the number of subjects has been small, interesting data was found that may lead to further, more extensive research. From the outcomes on user friendliness, for example, it appearsthat speech recognition is rated better by subjects with WMSD complaints. However, the number of subjects was too small to make group comparisons. It may be interesting for future research to compare subjects with and subjects without WMSD complaints as they use speech recognition software.5. ConclusionSpeech recognition software appears to improve working postures during VDU work and therefore, it may play a role in prevention of WMSD. However, it seems especially beneficial for people with WMSD complaints, who could continue working with the use of speech recognition. Further research is needed to study risk factors for vocal difficulties with the use of speech recognition. While current speech recognition is not recommended for general use, it may be a useful complement to other input devices for people without WMSD complaints. From the perspective of productivity and the perception of user friendliness further development of speech recognition software is necessary.ReferencesBaber, C., Noyes, J., 1996. Automatic speech recognition in adverse environments. Human Factors 38 (1), 142–155.Baber, C., Mellor, B., Graham, R., Noyes, J.M., Tunley, C., 1996. Workload and the use of automatic speech recognition: the effect of time and resource demands. Speech Commun. 20, 37–53.Bekker, M.M., Van Nes, F.L., Juola, J.F., 1995. A comparison of mouse and speech input control of a text-annotation system. Behaviour Inform. Technol. 14 (1), 14–22.Blatter, BM., Bongers, P.M., 1999. Work related neck and upper limb symptoms (RSI): high risk occupations and risk factors in the Dutch working population. Publ. no. r9800293. TNO Work and Employment, Hoofddorp.Blatter, B.M., Bongers, P.M., 2002. Duration of computer use and mouse use in relation to musculoskeletal disorders of neck or upper limb. Int. J. Ind. Ergon. 30, 295–306.Gerr, F., Marcus, M., Ensor, C., Kleinbaum, D., Cohen, S., Edwards, A., Gentry, E., Ortiz, D.J., Monteilh, C.A., 2002. Prospective study of computer users: I. Study design and incidence of musculoskeletal symptoms and disorders. Am. J. Ind. Med. 41 (4), 221–235.Van der Grinten, M.P., 1999. Arbouw-guidelines for physical load when using handtools and handling in packaging in the building industry. TNO Work and Employment, Hoofddorp, The Netherlands (In Dutch: Publ.no. R9800031/4070172. Confidential).Hagberg, M., Silverstein, B., Wells, R., Smith, M.J., Hendrick, H.W.,Carayon, P., Perusse, M., 1995. Work related musculoskeletal disorders (WMSDs): a reference book for prevention.Taylor & Francis Ltd., London.Hales, T.R., Bernard, B.P., 1996. Epidemiology of work-related musculoskeletal disorders. Orthop.Clin. N. Am. 27 (4), 679–709.Haxer, M.J., Guinn, L.W., Hogikyan, N.D., 2001. Use of speech recognition software: a vocalendurance test for the new millennium? J. Voice 15 (2), 231–236.Health Council of The Netherlands: RSI, 2000. The Hague: Health Council of The Netherlands, publication no. 2000/22 (In Dutch).Juul-Kristensen, B., Laursen, B., Pilegaard, M., Jensen, B.R., 2004. Physical workload during use of speech recognition and traditional input devices. Ergonomics 47 (2), 119–133.Kambeyanda, D., Singer, L., Cronk, S., 1997. Potential problems associated with use of speech recognition products. Assist. Technol. 9 (2), 95–101.Kilbom, A ., 2000. Repetitive work of the upper extremity: Part I guidelines for the practioner and Part II-the scienti?c basis for the guide. In: Mital, A., Kilbom, A? ., Kumar, S. (Eds.), Ergonomics Guidelines and Problem Solving. Elsvevier, Amsterdam.Marcus, M., Gerr, F., Monteilh, C., Ortiz, D.J., Gentry, E., Cohen, S., Edwards, A., Ensor, C., Kleinbaum, D., 2002. A prospective study of computer users: II postural risk factors for musculoskeletal symptoms and disorders. Am. J. Ind. Med. 41, 236–249.Noyes, J.M., Frankish, C.R., 1994. Errors and error correction in automatic speech recognition systems. Ergonomics 37 (11), 1943–1957.Office Ergonomics Research Committee, 1998. Musculoskeletal disorders in the office workplace.Findings of the Office Ergo-nomics Research Committee (OERC). Yarmouth Port, MA.Olson, D.E., Cruz, R.M., Izdebski, K., Baldwin, T., 2004. Muscle tension dysphonia in patients who use computerized speech recognition systems. Ear Nose Throat J. 83 (3), 195–198.Otten, F., Bongers, P., Houtman, I., 1998. The risk of developing RSI in The Netherlands, data form the continuous quality oflife survey, 1997. Maandbericht gezondheidsstatistiek 17, 5–19(In Dutch).Ponsioen, I.H.J., 1999. Automatic speech recognition: state-of-the-art and exploring the future.Tijdschrift voor Ergonomie, 140–147 (In Dutch).Punnett, L., Berqvist, U., 1997. Visual Display Unit Work and upper extremity musculoskeletal disorders. A review of epidemiological findings. National Institute for Working Life 1–160.Sluiter, J.K., Rest, K.M., Frings-Dresen, M.H., 2001. Criteria document for evaluating the work-relatedness of upper-extremity musculoskeletal disorders. Scand. J. Work Environ.Health. 27 (Suppl 1), 1–102.Tittiranonda, P., Burastero, S., Rempel, D., 1999. Risk factors for musculoskeletal disorders among computer users. Occupat. Med. 14 (1), 17–38.Werner, R., Armstrong, T., Bir, C., Aylard, M., 1997. Intracarpal canal pressures: the role of ?nger, hand, wrist and forearm position. Clin. Biomech. 12, 44–51.Williams, N.R., 2003. V oice recognition products-an occupational risk for users with ULDs?Occupat. Med. 53 (7), 452–455.语音识别在工作姿势、提高生产率和用户友好界面的作用摘要在评价语音识别对工作姿势、提高生产率和用户友好界面的作用上做了相当多的实验性的研究以及重复性测试。