中级声音统计的干扰是人类语音识别在自然噪音中的敏感性的基础
Alex C Clonan1,2,3, Xiu Zhai4, Ian H Stevenson2,5,3
1Electrical and Computer Engineering, University of Connecticut, Storrs, CT 06269.
bioRxiv : the preprint server for biology
|February 26, 2024
概括
人类听众使用中等水平的听觉统计数据来理解在杂环境中的语音. 这些统计线索可以阻碍或帮助语音识别,这取决于背景噪音的复杂性.
科学领域:
- 听觉神经科学 听觉神经科学
- 认知科学 认知科学
- 语音感知 语音感知
背景情况:
- 在噪音中识别语音至关重要,但具有挑战性.
- 听觉系统使用统计表示来处理声音.
- 统计线索对自然场景中的语音隔离的贡献尚不清楚.
研究的目的:
- 研究人类听众如何使用统计线索来隔离和识别环境噪音中的语音.
- 确定中级听觉表现在语音感知中的作用.
- 识别影响语音识别准确性的神经编码策略和统计线索.
主要方法:
- 利用了自然背景声音和变体,改变了光谱-时间统计数据.
- 开发了一个通用的感知回归框架,将神经模型统计数据与词识别准确性联系起来.
- 边缘耳模型与中级听觉中脑模型的预测能力的比较.
主要成果:
- 在不同的自然背景下,语音识别的准确性差异很大 (0%至100%).
- 背景噪音的总结统计数据可能会掩盖或揭露语音,影响识别.
- 使用总结统计数据的中级听觉中脑模型预测了90%以上的感知差异,超过了外围耳模型 (60%).
结论:
- 人类听众依赖于中级听觉统计数据来进行语音分离和自然噪音的识别.
- 感知涉及多个综合统计数据的有益或有害干扰.
- 中级的听觉表现是理解复杂声学环境中的语言的关键.
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