中级语音和噪音统计的干扰是人类语音识别的基础 自然环境噪音的敏感性
Alex C Clonan1,2,3, Xiu Zhai4, Ian H Stevenson2,3,5
1Electrical and Computer Engineering, University of Connecticut, Storrs, Connecticut 06269.
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. 这些统计线索可以根据背景噪音阻碍或帮助语音识别.
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:
- 在噪音中识别语音对于日常沟通至关重要.
- 人们越来越认识到听觉系统对声音感知的统计表示的使用.
- 统计线索和神经表征如何帮助复杂声音的语音分离,目前尚不清楚.
研究的目的:
- 调查人类听众如何利用统计线索来进行语音分离和识别环境噪音.
- 确定中级听觉表现在处理复杂的自然声音中的作用.
- 识别影响语音识别精度的神经编码策略和统计线索.
主要方法:
- 听众在自然和统计学扰乱的背景噪音中执行语音识别任务.
- 一般化感知回归框架是为了将神经模型统计数据与词识别精度联系起来而开发的.
- 用外围耳和中级听觉中脑模型来评估统计贡献.
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 (0-100%).
- 来自中级听觉中脑模型的总结统计数据预测了单次试验判断的准确率>90%,超过了外围耳模型 (60%的准确率).
- 总结统计数据的干扰可能会掩盖或揭露语音,影响识别.
结论:
- 人类在自然噪音中的语音感知依赖于中级听觉表现.
- 多个总结统计数据干扰,有利或不利地影响语音识别.
- 听觉中脑模型可以准确地预测复杂的听觉场景中的感知表现.
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