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emg2vec:基于肌电图的静音语音接口的自我监督预训

Qinhan Hou, Stefano van Gogh, Kevin Scheck

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|March 5, 2025

概括

这项研究介绍了emg2vec,这是一种使用电肌图 (EMG) 的静音语音接口的自我预训框架. 它显著提高了语音识别和合成准确性,特别是在有限的标记数据.

科学领域:

生物医学工程生物医学工程
计算机科学计算机科学
语音技术语言技术

背景情况:

无声语音接口 (SSI) 为声障碍者提供通信解决方案.
电肌图 (EMG) 是SSI的一个有希望的信号源,因为它的非侵入性和有效性.
现有的基于EMG的SSI方法通常需要大量的标记数据进行培训.

研究的目的:

提出和评估emg2vec,一个基于EMG的SSI的新型自我预训框架.
为了提高EMG-to-speech和EMG-to-text转换能力. 为了提高EMG-to-speech和EMG-to-text转换能力.
证明自我预训比传统的监督学习更有好处.

主要方法:

开发了emg2vec自我预训框架.
实施EMG转换为语音和EMG转换为文本的模型.
自我预训练与从头开始训练的实验比较,使用不同数量的标记数据.

主要成果:

使用emg2vec进行自我预训练显著提高了语音识别单词错误率 (WER) 7.32% (完整数据集) 和5.18% (20%数据).
语音合成也显示了改进,在使用20%的训练数据时获得了2.91%的收益.
与普通监督学习相比,该框架显示出更高的性能.

结论:

基于emg2vec的自我预训框架提高了基于EMG的静音语音接口的性能.
当标记数据稀缺时,自我预训特别有利于提高模型准确性.
这种方法为开发EMG-to-speech和EMG-to-text系统提供了更高效和有效的方法.

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