用六轴加速度计实现高精度无声语音识别，准确率达97.17%。
Silent Speech Sentence Recognition with Six-Axis Accelerometers using Conformer and CTC Algorithm
- 结合Conformer与CTC算法，从面部运动信号中解码无声语句
- 在数据库词表约束下，句子识别准确率达97.17%
- 适合语音障碍者使用，为无声交互提供新硬件方案
无声语音接口（SSI）正被积极研发，以帮助因沟通障碍而长期受困、生活质量下降的群体。然而，由于省略和连读现象，无声语句难以分割与识别。本文提出一种新型无声语句识别方法，利用六轴加速度计采集的面部运动信号，转换为转录的词与句子。采用基于Conformer的神经网络与连接时序分类（CTC）算法，实现上下文理解，并将非声学信号转化为词序列，仅依赖数据库中的候选词。测试结果显示，该方法在句子识别上达到97.17%的准确率，显著超越现有方法（典型准确率为85%-95%），证明加速度计作为高精度无声语音识别的可行模态。
原文摘要 · Abstract (English)
Silent speech interfaces (SSI) are being actively developed to assist individuals with communication impairments who have long suffered from daily hardships and a reduced quality of life. However, silent sentences are difficult to segment and recognize due to elision and linking. A novel silent speech sentence recognition method is proposed to convert the facial motion signals collected by six-axis accelerometers into transcribed words and sentences. A Conformer-based neural network with the Connectionist-Temporal-Classification algorithm is used to gain contextual understanding and translate the non-acoustic signals into words sequences, solely requesting the constituent words in the database. Test results show that the proposed method achieves a 97.17% accuracy in sentence recognition, surpassing the existing silent speech recognition methods with a typical accuracy of 85%-95%, and demonstrating the potential of accelerometers as an available SSI modality for high-accuracy silent speech sentence recognition.
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