Generalization of dimension-based statistical learning
Generalization of dimension-based statistical learning
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DOI：
10.3758/s13414-019-01956-5
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发表时间：
2020-05-01
影响因子：
1.7
通讯作者：
Holt, Lori L.
中科院分区：
文献类型：
--
作者：
Idemaru, Kaori;Holt, Lori L.
最近的研究表明，声学维度和语音类别之间的关系不是静态的。相反，它是受时空规律性的不断变化的分布所影响的，并且是特定于所经历的个体声音的。三项研究通过测试跨上下文学习的泛化，并测试诱导学习的更大单词列表的效果，研究了在线单词识别中人工重音[B]和[p]语音类别的感知，基于维度的统计学习的性质。研究结果表明，重音[B]和[p]的学习可以在不同的语境中推广，而对重音中没有经历过的语境的推广则更弱，即使是对于同一说话人所说的相同的语音类别[B]和[p]。结果支持一个丰富的模型的语音表示，是敏感的上下文相关的变化的方式的声学尺寸相关的语音类别。
Recent research demonstrates that the relationship between an acoustic dimension and speech categories is not static. Rather, it is influenced by the evolving distribution of dimensional regularity experienced across time, and specific to experienced individual sounds. Three studies examine the nature of this perceptual, dimension-based statistical learning of artificially accented [b] and [p] speech categories in online word recognition by testing generalization of learning across contexts, and testing the effect of a larger word list across which learning is induced. The results indicate that whereas learning of accented [b] and [p] generalizes across contexts, generalization to contexts not experienced in the accent is weaker even for the same speech categories [b] and [p] spoken by the same speaker. The results support a rich model of speech representation that is sensitive to context-dependent variation in the way the acoustic dimensions are related to speech categories.