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首页> 外文期刊>Neural Systems and Rehabilitation Engineering, IEEE Transactions on >A Voice-Input Voice-Output Communication Aid for People With Severe Speech Impairment
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A Voice-Input Voice-Output Communication Aid for People With Severe Speech Impairment

机译:严重语言障碍人士的语音输入语音输出通信帮助

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摘要

A new form of augmentative and alternative communication (AAC) device for people with severe speech impairment—the voice-input voice-output communication aid (VIVOCA)—is described. The VIVOCA recognizes the disordered speech of the user and builds messages, which are converted into synthetic speech. System development was carried out employing user-centered design and development methods, which identified and refined key requirements for the device. A novel methodology for building small vocabulary, speaker-dependent automatic speech recognizers with reduced amounts of training data, was applied. Experiments showed that this method is successful in generating good recognition performance (mean accuracy 96%) on highly disordered speech, even when recognition perplexity is increased. The selected message-building technique traded off various factors including speed of message construction and range of available message outputs. The VIVOCA was evaluated in a field trial by individuals with moderate to severe dysarthria and confirmed that they can make use of the device to produce intelligible speech output from disordered speech input. The trial highlighted some issues which limit the performance and usability of the device when applied in real usage situations, with mean recognition accuracy of 67% in these circumstances. These limitations will be addressed in future work.
机译:描述了一种针对严重语言障碍人士的新型形式的增强和替代通信(AAC)设备-语音输入语音输出通信辅助(VIVOCA)。 VIVOCA识别用户的语音混乱并构建消息,然后将其转换为合成语音。系统开发采用了以用户为中心的设计和开发方法,该方法确定并完善了该设备的关键要求。应用了一种新颖的方法,以减少训练数据量的方式建立小词汇量,取决于说话者的自动语音识别器。实验表明,即使增加识别的困惑度,该方法也可以成功地对高度混乱的语音产生良好的识别性能(平均准确度为96%)。选择的消息构建技术需要权衡各种因素,包括消息构建的速度和可用消息输出的范围。中重度构音障碍患者在现场试验中对VIVOCA进行了评估,并证实他们可以使用该设备从混乱的语音输入中产生可理解的语音输出。该试验突出了一些问题,这些问题限制了在实际使用情况下使用该设备的性能和可用性,在这些情况下的平均识别精度为67%。这些限制将在以后的工作中解决。

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