从尖到语音:NeuroVoc - 一个生物可信的语音编码框架用于听觉感知和耳植入模拟
Jacob de Nobel1, Jeroen J Briaire2, Thomas H W Bäck1
1Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, Leiden, Netherlands.
Hearing research
|November 15, 2025
概括
NeuroVoc从神经活动中重建语音波形,使正常听力和耳植入模型之间的比较成为可能. 这个语音编码器准确地反映了用于听力损失模拟的语音噪音性能.
科学领域:
- 听觉神经科学 听觉神经科学
- 语音处理 语音处理
- 信号处理 信号处理
背景情况:
- 模拟听觉感知对于理解听力损失和开发辅助技术至关重要.
- 当前的语音编码模型通常需要特定的语音编码策略,限制了灵活性.
- 直接比较正常听力和电气听力模型是具有挑战性的.
研究的目的:
- 推出NeuroVoc,一个灵活的,不依赖模型的语音编码框架.
- 为了从模拟的神经活动中重建声波形状.
- 为了促进正常听力和电气听力模型之间的直接比较.
主要方法:
- NeuroVoc在神经图表表示上使用逆富里埃转换 (时间频率内置的听觉神经纤维输出).
- 模块化架构允许轻松替换底层的听觉模型.
- 通过在线数字在噪音 (DIN) 测试来评估感知可理解性.
主要成果:
- 从正常听力和电听力模型中,NeuroVoc成功地重建了可理解的语音.
- 正常听力模型比电听力模型更好地保留了波结构.
- 使用电听模型进行语音编码的演讲,在统计学上显示了与耳植入物使用者的临床数据相当的性能.
结论:
- 在模拟听力损失中,NeuroVoc准确地反映了减少的语音噪音性能.
- 该框架支持用于耳植入物研究的灵活听觉模型比较.
- 这个语音编码器推进了正常听力和听力受损的听觉感知模拟.
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