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中北大学 2006 届本科毕业设计说明书 第 1 页 共 21 页 The effect of speech recognition on working postures, productivity and the perception of user friendliness Elsbeth M. de Korte Piet van Lingen Abstract A 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. Introduction Work-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 are 中北大学 2006 届本科毕业设计说明书 第 2 页 共 21 页 background 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 methods 2.1 Experimental design In 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. Subjects Fifteen 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 methods Working 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). 中北大学 2006 届本科毕业设计说明书 第 3 页 共 21 页 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 observed Fig. 1. Schematic drawing of camera placement, rear view. Table 1 Analysed postures (dominant side of the body) Body region Variables Neck Flexion: 25 Rotation: yes/no Shoulder Elevation (lifting): yes/no Upper arm Flexion (sagittal plane): 020 ； 20 Forearm Pronation/ supination/ neutralArm support: on work surface/ on armrests/ no support Wrist Flexion 030 / flexion30 / extension 030 /extension30 Radial deviation/ ulnar deviation/ no deviation postures 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 中北大学 2006 届本科毕业设计说明书 第 4 页 共 21 页 (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. Results 3.1. Working postures The working posture results (Fig. 3ah) 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. Productivity The 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 friendliness As can be seen from Table 2, learning to use speech recognition software was pretty difficult according to six subjects, pretty easy according to 8 subjects and very easy according to 1 subject. Nobody reported that the use of speech recognition software was very difficult. 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 notes, making long E-mails, editing text, making lists and changing between applications. Tasks that were reported to be unsuitable to be performed with speech 中北大学 2006 届本科毕业设计说明书 第 5 页 共 21 页 recognition 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. 3ah. Duration of postures in the first subtask using keyboard and mouse (KM) or speech recognition (SR) (*po0:05； *po0:01). 中北大学 2006 届本科毕业设计说明书 第 6 页 共 21 页 Fig. 4. Frequency of posture changes in the first subtask (*po0:05). Table 2 Perception of user friendliness of speech recognition outcomes related to gender, age and WMSD Subject Gender Age WMSD Difficulty to learn: Time to learn: Satisfied with Prefered input (years) Very easy Very much speech device Pretty easy Pretty much recognition Keyboard Pretty difficult Not so much Mouse Very difficult Speech Recognition 1 Female 30 No Pretty easy Not so much No Keyboard 2 Male 25 No Pretty easy Pretty much No Keyboard 3 Male 50 No Very easy Not so much Yes Speech recognition 4 Male 34 No Pretty difficult Not so much No Keyboard 5 Male 35 No Pretty easy Pretty much No Keyboard 6 Male 44 No Pretty difficult Not so much No Keyboard 7 Male 55 No Pretty difficult Pretty much Yes Speech recognition 8 Female 44 Yes Pretty easy Pretty much Yes Speech recognition 9 Female 42 Yes Pretty easy Not so much Yes Speech recognition 10 Female 49 Yes Pretty difficult Pretty much Yes Speech recognition 11 Female 42 Yes Pretty difficult Pretty much Yes Speech recognition 12 Female 54 Yes Pretty easy Pretty much Yes Missing 13 Male 40 Yes Pretty easy Pretty much No Keyboard 14 Male 41 Yes Pretty difficult Not so much Yes Mouse 15 Male 52 Yes Pretty easy Not so much No Keyboard 4. Discussion 4.1. Working postures The 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 in 中北大学 2006 届本科毕业设计说明书 第 7 页 共 21 页 semi-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 significant, 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. Productivity In 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 since 中北大学 2006 届本科毕业设计说明书 第 8 页 共 21 页 the 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 friendliness A 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 appears 中北大学 2006 届本科毕业设计说明书 第 9 页 共 21 页 that 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. Conclusion Speech 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. References Baber, C., Noyes, J., 1996. Automatic speech recognition in adverse environments. Human Factors 38 (1), 142155. 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, 3753. 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. 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Med. 53 (7), 452455. 中北大学 2006 届本科毕业设计说明书 第 12 页 共 21 页 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 。首先在对键盘和鼠标以及语音识别进行六个星期训练期以后 ,实现了十五个主题执行了一项规范化的任务。对语音识别的用途导致手腕的改善、姿势、前臂、膀臂和脖子运动的肩膀和改善当与对键盘和鼠标的用途比较。虽然观察方法是基本的 , 这项研究提供洞察入潜力好处语音识别有为姿势 的。但是 , 生产力被减少为多数主题和语音识别看来是能用的为具体任务唯一。从生产力透视和语音识别的用户友好界面看更加进一步的发展悟性软件是必要的。到现在 , 语音识别似乎特别有利于人们对 WMSD 的抱怨。 1 介绍 与工作相关的 musculoskeletal (WMSD)在 VDU用户 (办公室人体工程的研究Committee 1998 年之中 ； Gerr 等 2002 年 ； Blatter 和 Bongers 1999 年 ； 荷兰的健康委员会 , 2000 年 ； Otten 等 , 1998)中很普遍 。显示器单位 (VDU) 用户是在危险中开发脖子、肩膀、腕子和手怨言。 VDU 工作的期间 , 并且脖子笨拙姿势 , 肩膀、腕子和手是重要风险因素。并且 , 缺乏胳膊支持也许导致脖子、肩膀、胳膊和手怨言 (Punnett 和 Berqvist 1997 年 ； Hales 和 Bernard 1996 年 ； Marcus 等2002 年 ； Blatter 和 Bongers 2002 年 ； Otten etal. 1998 年 ； Tittiranonda 等 1999 年 ； 办公室人体工程的研究委员会 , 1998) 。 以连续的新一代语音识别 软件的到来 , 使语音识别作为一新输入装置变得有趣。它可能替换鼠标和键盘。此外 , 因为语音识别软件可能实现无手管理 , 它使用户自由地搬走在工作场所。避免笨拙姿势成为可能。所以 , 它是有趣探索是否语音识别可能减少 WMSD 风险因素的当中一个 , 是笨拙工作姿势 , 并且是否它也许充当在预防的一个角色。但是 , 对这个题目的研究是有限的。 语音识别和用户友好界面及生产力之间的联系早已研究过 (Baber 和 Noyes 1996 年 ； Baber 等 1996 年 ； Ponsioen 1999 年 ； Bekker 等 1995 年 ； Noyes 和法兰克人 , 1994) 。这些研究表示 , 讲话的认识的质量依靠几个因素 , 例如声音用户 , 中北大学 2006 届本科毕业设计说明书 第 13 页 共 21 页 被情感、重音、寒冷或疲劳影响。其它被发现的重要因素是背景噪声和相当数量训练使用语音识别。当认识质量减少 , 错误的数量增加 , 消极地影响用户生产力。改正错误用正确的方式非常重要 : 系统需要由用户经常训练保留质量和改进系统。由于生产力和用户友好界面确实在对语音识别软件的实际购买应用到大规模范围 ,在这项研究中介入这些因素就显得重要了。 这项研究被设计评估在 VDU 工作期间对语音识别在工作姿势、生产力和用户友好界面的用途与对传统键盘和鼠标的用途比较。 2 主题和方法 2.1 实验性设计 在相当多的实验性研究及重复的测试对两项输入装置进行测试。键盘和鼠标的传统组合与语音识别比较各自在前和进一步测验。在预告测验以后 , 主题接受了训练在使用语音识别由公司被专门研究语音识别训练。在 6 个星期期间他们的每日工作所有主题使用了语音识别 (龙自然地讲荷兰语 3.6, 对应于英语 3.6 版本 )。在 6 个星期以后 , 进一步测验进行。工作姿势和生产力被测量了并且用户友好度得到了评定估计。 在他们自己的工作站 , 主题执行了包括二个子任务 的一项规范化的计算机任务。第一子任务包括的制造和发送在 Microsoft Outlook 为 5min 的电子邮件 ,在Microsoft Word 为 9min的文字处理和正文编辑 ,和改变在申请之间 (微软擅长 , 力量点和 Internet Explorer) 对 1min。这个子任务的总期间是 15 分钟。在这子任务期间工作姿势被记录了在录影带。在预告测验主题使用了键盘和鼠标。在进一步测验他们被允许使用键盘和鼠标 ；要求他们仅仅以语音识别执行第一子任务是不现实的。 第二个子任务包括复制没有编辑的文本。这项任务执行的最大值为 10min 。主题被指示运作在他们自己的节奏。没有指示显示在改正错误上。第二个子任务使用测量生产力。结果 , 主题被限于语音识别的进一步测验中。 2.2 主题 9 名男子和 6 名妇女对十五个主题参加了研究。他们全都在荷兰部运作社会中北大学 2006 届本科毕业设计说明书 第 14 页 共 21 页 事务。主题是老练的 VDU 工作者并且他们的每日工作至少 50%包括了 VDU 工作。他们是所有非接触打字员。八个主题遭受了 WMSD 。七个主题免于伤害。他们的年龄变化在 25 和 55 年 (手段 42.5, SD 8.8 之间 ) 。在实验之前 , 参加者允许被通告。 2.3 测量的方法 在总期 间 (15 分钟 )的第一子任务里工作姿势被记录在录影带。照相机被安置了在主题的显要一边 (Figs. 1) 。 图 1. 照相机安置 , 背面图概要图画 与观察员 (Noldus) 对于姿势的第一个情况的图象分析 (总时间的百分比 )的改变的姿势 (变动的数字期间且频率每分钟 ) 。姿势的观察被采取了物理装载的指南 , 由 TNO 工作 & 就业 (van der Grinten 1999) 开发。因变量被显示在表 1 。以 T 测试对工作姿势上的区别作重复的测试。意义水平 (a) 被设置了在 5%(双面 ) 。 Table 1 Analysed postures (dominant side of the body) Body region Variables Neck Flexion: 25 Rotation: yes/no Shoulder Elevation (lifting): yes/no Upper arm Flexion (sagittal plane): 020 ； 20 Forearm Pronation/ supination/ neutralArm support: on work surface/ on armrests/ no 中北大学 2006 届本科毕业设计说明书 第 15 页 共 21 页 support Wrist Flexion 030 / flexion30 / extension 030 /extension30 Radial deviation/ ulnar deviation/ no deviation 生产力测量被采取了第二个子任务。与观察员 (Noldus) 录影带分析对于错误的数字每分钟和时间在改正上花费错误 (总时间的百分比 ) 。此外 ,每分钟输入正确的数量的词对文本进行分析。在生产力上的区别了以 T 测试为重覆的措施做测试。最后 , 被察觉的生产力产生出问题 : 您完成了更多 , 或同样相当数量工作自从您开始使用语音识别 ? 对这个问题的答复被提出作为频率。 语音识别的用户友好的悟性被估计了与查询表。这 张查询表目标在于语音识别在他们的每日工作的 6 个星期中主题的使用。它被提出了在进一步测验以后。问题询问了关于它将学会使用语音识别的困难 , 多长时间需要学会它和特选为输入装置 (键盘、鼠标或语音识别 ) 。此外 , 他们适合执行语音识别的任务的主题不是请求命名计算机任务。终于 , 主题被询问他们是否对使用语音识别感到满意。数据从查询表被提出作为频率。组织了两次会议交换经验。首次会议被组织在训练期的第三个星期为了与语音识别软件分享第一经验。第二次会议被组织了在实验发生为了评估对语音识别软件的用途和对交换意见之后。信息来自 这些会议并且被报告。 3.结论 3.1 工作姿势 工作姿势结果 (图 3.a-h) 显示 , 通过语音识别的用途与键盘和鼠标的对比 , 极大减少了脖子的弯曲、前臂的内旋和手腕骨的背离。以对语音识别胳膊的用途支持与键盘和鼠标的用途做了更多的比较。 从图 4 能看出 , 在语音识别的应用中 ,在脖子弯曲姿势、脖子自转姿势和胳膊支持与对键盘和鼠标的用途比较中没有发现较大的变动。当语音识别被使用了 , 更多变化被发现了在膀臂的弯曲姿势上。 3.2 生产力 词的数量正确地输入每分钟是显着大的以对键盘和鼠标 (手段 22.7, SD 6.7 中北大学 2006 届本科毕业设计说明书 第 16 页 共 21 页 的 ) 用途与对语音识别比较 (手段 17.5, SD 8.8 的 ) 用途。虽然在错误频率上的重大区别未被发现 , 但花费在修正错误上的时间和语音识别的使用一样长。以对键盘和鼠标 4.2% 的用途时间与花费在错修正 52.2%对比的语音识别的用途。为生产力测量 ,在主题之间发现了大的变异 , 他们是一样有生产力的以使用语音识别象他们以使用键盘和鼠标 , 5 个主题是没有生产力的并且一个主题是更加有生产力的。 3.3 用户友好的悟性 从表 2 能看出 , 学会使用语音识别软件是 “相当困难 ”根据六个主题 , “相当容易 ”根据 8 个主题和 “非常容易 ”根据 1 个主题。没人报告 , 对语音识别软件的用途是 “非常困难 ”。时间主题花费在学会使用语音识别上几乎被描述了 “很非常 ”作为8 个主题和 “不非常 ”作为 7 主题。没人花费了 “非常多 ”时间。主题的多数以 WMSD 怨言满足了对语音识别的用途 , 与伤害主题对比。主题以 WMSD 怨言比任意是伤害的主题经常并且更喜欢语音识别当输入装置。 被报告是适合用语音识别执行的计算机任务是字词处理 , 做笔记的 , 做长的电子邮件 , 编辑文本 , 做名单和改变在应用之间。被报告不适合以语音识别的执行任务运作在报表里譬如擅长 , 字词处理 在一种外语 , 做短的电子邮件 , 做桌和图 , 改正文本 , 运作以统计应用譬如 SPSS 和处理日历。 从会议看起来 , 语音识别被赞赏了作为一种补全输入装置因为它为用户提供可变化的方法使他们与计算机相处融洽。为遭受 WMSD 怨言语音识别的主题使他们依然是工作。对语音识别的用途的一重要消极后果是 , 五个主题报告有疼痛喉头。 中北大学 2006 届本科毕业设计说明书 第 17 页 共 21 页 图 3 a-h 姿势的期间在第一子任务使用键盘和鼠标 (KM) 或语音识别 (SR) (* po0:05； * po0:01) 图 4 姿势变化频率在第一子任务上 (* po0:05) 语音识别结果的用户友好的悟性与性别、年龄和 WMSD 关系 Subject Gender Age WMSD Difficulty to learn: Time to learn: Satisfied with Prefered input (years) Very easy Very much speech device Pretty easy Pretty much recognition Keyboard Pretty difficult Not so much Mo