婴儿语音学习作为感知空间学习:对计算模型的跨语言评估
Yevgen Matusevych1,2, Thomas Schatz3, Herman Kamper4
1School of Informatics, University of Edinburgh.
Cognitive science
|July 18, 2023
概括
婴儿通过感知空间学习来适应母语的声音,而不仅仅是语音类别. 使用声学或词级数据的模型解释了婴儿语音感知中的跨语言差异.
科学领域:
- 认知科学 认知科学
- 发展心理学 发展心理学
- 语言学的语言学.
背景情况:
- 婴儿的语音感知在生命的第一年内调整为他们的母语.
- 这种调整传统上是通过声学类别获取来解释的.
- 替代理论提出没有明确类别的感知空间学习.
研究的目的:
- 研究感知空间学习作为早期语音学习的模型.
- 实施和测试感知空间学习的计算模型.
- 为了比较模型的表现与婴儿语音感知中的既定发现.
主要方法:
- 实施了五种不同的感知空间学习模型.
- 测试了婴儿语音感知文学中的三个音声对比的模型.
- 评估模型复制跨语言差异的能力.
主要成果:
- 使用分布式声学信息的模型解释了一些跨语言差异.
- 第二个采用单词级指导的模型表现相似.
- 这两种模型都支持感知空间学习,但需要进一步改进.
结论:
- 感知空间学习为早期的语音学习提供了一个可行的框架.
- 在这个过程中,声学和词级信息都很重要.
- 未来的研究需要更精细的数据来区分特定的感知空间学习模型.
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