通过基于集成文本和语音表示的表现学习方法来改善电喉语音增强
概括
这项研究引入了一种新的方法,通过整合文本信息,将电喉 (EL) 语音转换为正常语音. 这种方法显著提高了喉切除患者的言语自然性和可理解性.
科学领域:
- 语音处理 语音处理
- 人工智能的人工智能是人工智能.
- 生物医学工程 生物医学工程
背景情况:
- 喉切除术所使用的电喉 (EL) 语音缺乏自然性和可理解性,原因是与正常语音的音调和时间偏差.
- 现有的EL-speech-to-normal-speech转换 (EL2SP) 方法使用序列到序列的语音转换 (VC) 扎在特征不匹配,导致累积的错误和有限的性能.
研究的目的:
- 为EL2SP开发一种新的表示学习方法,通过整合对联的EL语音和文本表示来提高映射精度.
- 为了更好的沟通,提高EL语音的自然性和可理解性.
主要方法:
- 提出了一种新的表示学习方法,将配对的电喉语音和文本表示集成在一起.
- 嵌入文本信息以丰富中间表示,并指导语音编码器和解码器之间的对齐学习.
- 应用自编码器式重建培训,在不增加模型复杂性的情况下继承集成表示.
主要成果:
- 拟议的方法与数据增强相结合,超过了仅学习语音表示的基线方法.
- 与预训练-微调基线相比,在字符错误率上取得了19.2%的改善.
- 在自然度得分上表现出0.67的增加,验证了综合表示方法的有效性.
结论:
- 将文本信息整合到表达式学习中,显著提高了EL语言到正常语言的转换.
- 拟议的方法提供了一种更有效的方法,以改善喉切除患者的EL语音质量.
- 这种技术有望促进语音障碍患者的辅助通信技术的发展.
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