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Discovering Words in Fluent Speech: The Contribution of Two Kinds of Statistical Information

机译:在流利的语音中发现单词:两种统计信息的贡献

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To efficiently segment fluent speech, infants must discover the predominant phonological form of words in the native language. In English, for example, content words typically begin with a stressed syllable. To discover this regularity, infants need to identify a set of words. We propose that statistical learning plays two roles in this process. First, it provides a cue that allows infants to segment words from fluent speech, even without language-specific phonological knowledge. Second, once infants have identified a set of lexical forms, they can learn from the distribution of acoustic features across those word forms. The current experiments demonstrate both processes are available to 5-month-old infants. This demonstration of sensitivity to statistical structure in speech, weighted more heavily than phonological cues to segmentation at an early age, is consistent with theoretical accounts that claim statistical learning plays a role in helping infants to adapt to the structure of their native language from very early in life.
机译:为了有效地分割流利的语音,婴儿必须发现母语中单词的主要语音形式。例如,在英语中,内容词通常以重读音节开头。为了发现这种规律性,婴儿需要识别一组单词。我们建议统计学习在此过程中扮演两个角色。首先,它提供了一种提示,即使没有特定语言的语音学知识,也可以使婴儿从流利的语音中分割单词。其次,一旦婴儿识别出一组词汇形式,他们便可以从这些单词形式的声学特征分布中学习。当前的实验表明这两种方法都适用于5个月大的婴儿。这种对语音统计结构敏感性的证明,比语音提示在早期就更重要,这与理论上的说法相吻合,该理论认为统计学习在帮助婴儿从很早就适应其母语结构方面就发挥了作用在生活中。

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