Ambiguity, competition, and blending in spoken word recognition
Ambiguity, competition, and blending in spoken word recognition
复制标题
DOI：
10.1207/s15516709cog2304_3
复制
发表时间：
1999-10-01
影响因子：
2.5
通讯作者：
Marslen-Wilson, WD
中科院分区：
文献类型：
--
作者：
Gaskell, MG;Marslen-Wilson, WD
语音信号的瞬时模糊性是感知口语的关键特性。在言语感知的地方主义模型中，这种歧义是通过允许多个词汇表征的平行激活来捕获的。本文探讨了如何分布式模型的语音感知CON容纳这一属性。向量空间的统计分析表明，多个分布式表示的协同激活本质上是噪声的，并且依赖于诸如稀疏性和维数等参数。此外，共激活的特点有很大的不同，这取决于组织的心理词典内的分布式表征。这一观点的词汇访问支持的语音和语义词表征的分析，这提供了一个解释最近的一组实验在言语知觉的共激活。
A critical property of the perception of spoken words is the transient ambiguity of the speech signal. In localist models of speech perception this ambiguity is captured by allowing the parallel activation of multiple lexical representations. This paper examines how a distributed model of speech perception con accommodate this property. Statistical analyses of vector spaces show that coactivation of multiple distributed representations is inherently noisy, and depends on parameters such as sparseness and dimensionality. Furthermore, the characteristics of coactivation vary considerably, depending on the organization of distributed representations within the mental lexicon. This view of lexical access is supported by analyses of phonological and semantic word representations, which provide an explanation of a recent set of experiments on coactivation in speech perception.