A quantitative model of the language familiarity effect in infancy
A quantitative model of the language familiarity effect in infancy
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DOI：
10.32470/ccn.2019.1353-0
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Craig A. Thorburn;Naomi H Feldman;Thomas Schatz
中科院分区：
文献类型：
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作者：
Craig A. Thorburn;Naomi H Feldman;Thomas Schatz
人类听众比其他语言的人更善于区分母语的说话者，这种现象被称为语言熟悉效应。最近在4.5个月大的婴儿身上观察到的这种效应（Fecher &约翰逊，出版中）给这种效应的理论带来了新的困难。一方面，保留经典的帐户依赖于母语的复杂知识（Goggin，Thompson，Strube和Simental，1991）-需要解释婴儿如何能够如此早地获得这种知识。另一方面，放弃这些解释需要解释在缺乏这种知识的情况下如何产生效果。在本文中，我们基于无监督机器学习和零资源语音技术的算法，首次提出了一种可行的婴儿语言熟悉度效应的习得机制。我们的研究结果表明，在不依赖于复杂的语言知识，婴儿可以通过统计建模在多个时间尺度的语音信号的声学，他们所暴露的语言熟悉的效果。
Human listeners are better at telling apart speakers of their native language than speakers of other languages, a phenomenon known as the language familiarity effect. The recent observation of such an effect in infants as young as 4.5 months of age (Fecher & Johnson, in press) has led to new difficulties for theories of the effect. On the one hand, retaining classical accounts—which rely on sophisticated knowledge of the native language (Goggin, Thompson, Strube, & Simental, 1991)–requires an explanation of how infants could acquire this knowledge so early. On the other hand, letting go of these accounts requires an explanation of how the effect could arise in the absence of such knowledge. In this paper, we build on algorithms from unsupervised machine learning and zero-resource speech technology to propose, for the first time, a feasible acquisition mechanism for the language familiarity effect in infants. Our results show how, without relying on sophisticated linguistic knowledge, infants could develop a language familiarity effect through statistical modeling at multiple time-scales of the acoustics of the speech signal to which they are exposed.