LEAD-CNRS, Université de Bourgogne, Dijon LPC-CNRS, Université de Provence, Marseille.
Cogn Sci. 2009 Mar;33(2):260-72. doi: 10.1111/j.1551-6709.2009.01012.x.
Saffran, Newport, and Aslin (1996a) found that human infants are sensitive to statistical regularities corresponding to lexical units when hearing an artificial spoken language. Two sorts of segmentation strategies have been proposed to account for this early word-segmentation ability: bracketing strategies, in which infants are assumed to insert boundaries into continuous speech, and clustering strategies, in which infants are assumed to group certain speech sequences together into units (Swingley, 2005). In the present study, we test the predictions of two computational models instantiating each of these strategies i.e., Serial Recurrent Networks: Elman, 1990; and Parser: Perruchet & Vinter, 1998 in an experiment where we compare the lexical and sublexical recognition performance of adults after hearing 2 or 10 min of an artificial spoken language. The results are consistent with Parser's predictions and the clustering approach, showing that performance on words is better than performance on part-words only after 10 min. This result suggests that word segmentation abilities are not merely due to stronger associations between sublexical units but to the emergence of stronger lexical representations during the development of speech perception processes.
萨弗兰、纽波特和阿斯林（1996a）发现，人类婴儿在听到人工语言时，对与词汇单位相对应的统计规律很敏感。有两种分割策略被提出以解释这种早期的单词分割能力：括号策略，即婴儿被假设在连续的语音中插入边界；聚类策略，即婴儿被假设将某些语音序列组合在一起成单元（斯温格利，2005）。在本研究中，我们测试了两种计算模型的预测，这两种模型分别体现了这两种策略，即：序列递归网络：埃尔曼，1990 年；和解析器：佩鲁切特和文特尔，1998 年，在一个实验中，我们比较了成年人在听到 2 或 10 分钟的人工语言后在词汇和亚词汇识别方面的表现。结果与解析器的预测和聚类方法一致，表明只有在 10 分钟后，单词的表现才优于部分单词的表现。这一结果表明，单词分割能力不仅仅是由于亚词汇单元之间更强的关联，而是由于在语音感知过程的发展过程中出现了更强的词汇表示。
