从对话者之间的距离来预测相对可理解性,在语音感知相似空间中预测语音的相对可理解性
Seung-Eun Kim1, Bronya R Chernyak2, Joseph Keshet2
1Department of Linguistics, Northwestern University, 2016 Sheridan Road, Evanston, IL, 60208, USA. seungeun.kim@northwestern.edu.
Psychonomic bulletin & review
|February 10, 2025
概括
这项研究引入了一种新的方法来理解语音感知,使用机器学习来分析说话者可理解性. 感知相似空间有效地预测了说话者如何理解,超过了传统方法.
科学领域:
- 语音感知 语音感知
- 声学语音学的声音学
- 机器学习 机器学习
背景情况:
- 传统的语音感知模型依赖于对声学语音线索的组成处理.
- 这些模型不足以解释说话者理解度的变化.
- 需要一种替代方法来捕捉整体的语音感知.
研究的目的:
- 用自我监督学习来估计语言的感知表现.
- 调查感知相似空间和说话者的可理解性之间的关系.
- 将这种新方法的预测能力与传统的声学语音线索进行比较.
主要方法:
- 通过语音噪音测试评估了114名L2和25名L1英语说话者的可理解性.
- 利用自我监督学习来生成语音样本的表示.
- 在感知相似空间中分析了语音轨迹之间的整体距离.
主要成果:
- 感知相似空间中的距离测量预测了L2英语说话者的相对可理解性.
- 这项测量比传统的声声语音线索更好地预测L2可理解性.
- 距离测量还解释了L1英语说话者之间的一些可理解性差异.
结论:
- 感知相似空间方法提供了一种更有效的方法来捕捉说话者的可理解性.
- 这种方法为人类语音产生和感知的变化提供了有价值的见解.
- 自主监督学习是分析复杂语音特征的有希望的工具.
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