学习理解不熟悉的说话者:测试分布式学习作为快速适应性语音感知模型
Maryann Tan1, T Florian Jaeger2
1Centre for Research on Bilingualism, Stockholm University, Sweden; Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, United States of America.
Cognition
|August 9, 2025
概括
人类的语音感知迅速适应新的口音. 然而,这项研究揭示了适应性高原,这表明听众如何学习不熟悉的语音模式的限制.
科学领域:
- 认知科学 认知科学
- 心理语言学 心理语言学
- 听觉神经科学 听觉神经科学
背景情况:
- 人类的语音感知非常适应,很快适应不熟悉的口音.
- 快速语音适应的基础上的增量过程仍然不太清楚.
- 关键问题涉及将以前的经验与新的语音输入以及适应的速度/成功相结合.
研究的目的:
- 研究语音感知中的快速语音适应的增量机制.
- 模拟听众如何将先前的期望与不熟悉的口音暴露相结合.
- 为了确定适应性语音感知上的限制.
主要方法:
- 在美国英语听众中采用增量暴露测试范式.
- 让参与者接触到单词初始 /d/ 和 /t/ 声音的音调分布的变化.
- 利用贝叶斯混合效应心理测量模型进行数据分析并与理想学习者模型进行比较.
主要成果:
- 一个分布式学习模型准确地捕捉了听者的先前感知和适应模式 (R2>96%).
- 听众的感知变化停滞在成功的学习模型预测的水平以下.
- 确定了以前未被识别的对语音适应程度的约束.
结论:
- 分布式学习有效地模拟了自适应性语音感知方面的方面.
- 听众适应不熟悉的语音模式似乎具有固有的局限性.
- 需要进一步的研究来了解人类语音感知适应性的限制.
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