端到端的视听学习用于耳植入物声音编码模拟在杂的环境中
Meng-Ping Lin1, Enoch Hsin-Ho Huang2, Shao-Yi Chien1
1Graduate Institute of Electronics Engineering and the Department of Electrical Engineering, National Taiwan University, Taipei 106319, Taiwan.
JASA express letters
|January 9, 2026
概括
使用深度学习将视觉线索与耳植入物 (CI) 声音编码相结合,可以显著提高噪音中的语音可理解性. 这种新的视听语音增强系统将信号对错误比提高7.47dB.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 听觉神经科学 听觉神经科学
背景情况:
- 耳植入物 (CI) 通过电刺激恢复严重到严重的听力损失的听力.
- 在杂的环境中倾听仍然是CI用户面临的重大挑战.
- 深度学习为CI声音编码提供了新的方法,特别是通过整合视觉信息.
研究的目的:
- 开发和评估一个端到端的CI系统,将视听语音增强 (AVSE) 模块与ElectrodeNet-CS (ECS) 模型集成在一起.
- 评估系统在模拟噪音条件下提高语音可理解性和信号质量的性能.
主要方法:
- 一个AVSE模块与ECS模型集成,以创建AVSE-ECS系统.
- 为了优化其性能,AVSE-ECS系统接受了联合培训.
- 客观语音理解度指标和信号对错误比率 (SER) 用于评估.
主要成果:
- 通过AVSE-ECS系统,高客观语音可理解性得到了证明.
- 与先进的组合编码器策略相比,信号与错误比率 (SER) 的显著改善为7.4666dB.
- 对AVSE-ECS系统的联合培训产生了卓越的结果.
结论:
- 集成AVSE模块显示出对推进CI声音编码策略的重大前景.
- 这种视听方法提供了一个潜在的解决方案,以改善CI用户在噪音中的语音感知.
- 在开发更有效的耳植入物技术方面,AVSE-ECS系统代表了一步前进.
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