在没有语言类别的情况下模拟早期的语音和单词学习
Marvin Lavechin1, Maureen de Seyssel2,3, Hadrien Titeux2
1GIPSA-lab, Université Grenoble Alpes, Grenoble, France.
Developmental science
|January 7, 2025
概括
婴儿学习语音和单词,而不需要像语音这样的语言类别. 一个名为STELA的新算法表明,从原始语音中提取统计模式可以解释早期的语言学习,这表明类别是以后出现的.
科学领域:
- 认知科学 认知科学
- 发展心理学 发展心理学
- 计算语言学 计算语言学
背景情况:
- 婴儿在说话之前就对母语的声音和单词形式产生了敏感性.
- 传统观点将这些变化归因于获得语言类别 (语音,单词).
- 由于婴儿早期类别知识的证据有限,存在怀疑.
研究的目的:
- 调查早期单词学习是否需要语言类别.
- 提出和测试一个计算模型,解释婴儿的语言感知发展.
- 挑战早期语言学习中先前存在的语言类别的必要性.
主要方法:
- 介绍STELA,一个预测编码算法.
- STELA从连续的原始语音数据中提取统计模式.
- 分析学习的表征,以了解新出现的属性.
主要成果:
- STELA成功地重现了语音和词形学习中的发育模式.
- 该算法在不依赖于明确的语言类别 (语音,单词) 的情况下运行.
- 对于STELA的学习,不需要明确的单词细分要求.
结论:
- 语言类别可能不是早期语言学习的先决条件.
- 从连续语音中提取统计模式是早期学习的一个可行的机制.
- 语言类别可能是学习的结果,而不是最初的要求.
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