一个跨主题的sEMG-to-speech转换系统,使用内容特征和模型校准.
概括
本研究介绍了一种新型的表面电肌造影到语音 (ETS) 系统,该系统通过要求更少的数据和消除同步语音录音的需求,从而减少了用户负担. 该系统实现了对言语障碍患者的有效语音生成,即使在使用合成音频进行训练时也是如此.
科学领域:
- 生物医学工程 生物医学工程
- 语音技术 语言技术
- 机器学习 机器学习
背景情况:
- 目前的表面电肌图转化为语音 (sEMG-ETS) 系统需要广泛的单参与者数据集和同步语音录音以进行培训.
- 这些局限性阻碍了实际应用,特别是对于言语障碍的人来说.
研究的目的:
- 开发一个跨学科的sEMG-to-speech (ETS) 转换系统,尽量减少用户数据需求和培训负担.
- 通过使用合成音频进行模型培训,为面临语言障碍的人提供ETS技术.
主要方法:
- 使用预训练的声学模型来提取扬声器独立的声学特征.
- 使用儿童调模型校准,以适应声学模型的新用户的EMG功能.
- 研究了电子合成音频用于训练ETS模型的使用,作为人类语音录音的替代品.
主要成果:
- 拟议的sEMG-to-speech (ETS) 转换系统在新用户的校准数据仅20分钟后,实现了21.71%的字符错误率 (CER).
- 使用合成音频训练ETS模型的CER结果与使用人类语音数据进行训练相似.
结论:
- 开发的跨主题ETS系统显著降低了用户的数据负担.
- 合成音频的使用为训练ETS模型提供了可行的和有效的替代方案,扩大了言语障碍患者的可访问性.
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