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Development of sEMG sensors and algorithms for silent speech recognition

机译:用于无声语音识别的sEMG传感器和算法的开发

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Objective. Speech is among the most natural forms of human communication, thereby offering an attractive modality for human-machine interaction through automatic speech recognition (ASR). However, the limitations of ASR-including degradation in the presence of ambient noise, limited privacy and poor accessibility for those with significant speech disorders-have motivated the need for alternative non-acoustic modalities of subvocal or silent speech recognition (SSR). Approach. We have developed a new system of face- and neck-worn sensors and signal processing algorithms that are capable of recognizing silently mouthed words and phrases entirely from the surface electromyographic (sEMG) signals recorded from muscles of the face and neck that are involved in the production of speech. The algorithms were strategically developed by evolving speech recognition models: first for recognizing isolated words by extracting speech-related features from sEMG signals, then for recognizing sequences of words from patterns of sEMG signals using grammar models, and finally for recognizing a vocabulary of previously untrained words using phoneme-based models. The final recognition algorithms were integrated with specially designed multi-point, miniaturized sensors that can be arranged in flexible geometries to record high-fidelity sEMG signal measurements from small articulator muscles of the face and neck. Main results. We tested the system of sensors and algorithms during a series of subvocal speech experiments involving more than 1200 phrases generated from a 2200-word vocabulary and achieved an 8.9%-word error rate (91.1% recognition rate), far surpassing previous attempts in the field. Significance. These results demonstrate the viability of our system as an alternative modality of communication for a multitude of applications including: persons with speech impairments following a laryngectomy; military personnel requiring hands-free covert communication; or the consumer in need of privacy while speaking on a mobile phone in public. electromyography; speech recognition; facial prosthesis; alternative communication; human-machine interface
机译:目的。语音是人类交流的最自然形式之一,从而通过自动语音识别(ASR)为人机交互提供了一种有吸引力的方式。但是,ASR的局限性(包括在存在环境噪声的情况下性能下降,隐私受到限制以及严重的言语障碍人群的可及性差)促使人们需要使用替代的非声学模式的人声或无声语音识别(SSR)。方法。我们开发了一种新的面部和颈部磨损传感器系统以及信号处理算法,能够完全从面部和颈部肌肉中所记录的表面肌电图(sEMG)信号中识别出无声的嘴巴单词和短语。演讲的产生。该算法是通过发展语音识别模型来策略性开发的:首先用于通过从sEMG信号中提取与语音相关的特征来识别孤立的单词,然后用于使用语法模型从sEMG信号的模式中识别单词序列,最后用于识别以前未经训练的词汇使用基于音素模型的单词。最终识别算法与专门设计的多点微型传感器集成在一起,这些传感器可以按灵活的几何形状排列,以记录来自面部和颈部小关节肌的高保真sEMG信号测量结果。主要结果。我们在一系列次声语音实验中测试了传感器和算法系统,这些实验涉及从2200个单词的词汇中生成的1200多个短语,并实现了8.9%的单词错误率(91.1%识别率),远远超出了该领域的先前尝试。 。意义。这些结果证明了我们的系统作为多种应用的替代通信方式的可行性,这些应用包括:喉切除术后有语言障碍的人;要求免提秘密通信的军事人员;或在公共场所用手机通话时需要隐私的消费者。肌电图语音识别;面部假体替代沟通;人机接口

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  • 来源
    《Journal of neural engineering 》 |2018年第4期| 046031.1-046031.11| 共11页
  • 作者单位

    VocaliD, Inc. 50 Leonard St, Belmont, MA 02478, United States of America;

    Harvard Medical School Department of Surgery, Massachusetts General Hospital, Boston, United States of America;

    BAE Systems Inc, Burlington, MA, United States of America;

    Delsys, Inc and Altec, Inc, 23 Strathmore Rd, Natick, MA 01760, United States of America;

    Delsys, Inc and Altec, Inc, 23 Strathmore Rd, Natick, MA 01760, United States of America;

    Delsys, Inc and Altec, Inc, 23 Strathmore Rd, Natick, MA 01760, United States of America;

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