Speech is continuous, and isolating meaningful chunks for lexical access is a nontrivial problem. In this paper we use neural network models and more conventional statistics to study the use of sequential phonological probabilities in the segmentation of an idealized phonological transcription of the London-Lund Corpus; these speech data are representative of genuine conversational English. We demonstrate, first, that the distribution of phonetic segments in English is an important cue to segmentation, and, second, that the distributional information is such that it might allow the infant, beginning with only a sensitivity to the statistics of subsegmental primitives, to bootstrap into a series of increasingly sophisticated segmentation competences, ending with an adult competence. We discuss the relation between the behavior of the models and existing psycholinguistic studies of speech segmentation. In particular, we confirm the utility of the Metrical Segmentation Strategy (Cutler & Norris, 1988) and demonstrate a route by which this utility might be recognized by the infant, without requiring the prior specification of categories like "syllable" or "strong syllable."

中文翻译：

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Bootstrapping Word Boundaries：一种自下而上的基于语料库的语音分割方法

语音是连续的，为词汇访问隔离有意义的块是一个重要的问题。在本文中，我们使用神经网络模型和更传统的统计数据来研究连续音系概率在伦敦-隆德语料库的理想化音系转录的分割中的使用；这些语音数据代表了真正的英语会话。我们证明，首先，英语语音段的分布是分割的一个重要线索，其次，分布信息可能允许婴儿从对子段原语的统计数据开始敏感，引导到一系列日益复杂的分割能力，以成人能力结束。我们讨论模型的行为与语音分割的现有心理语言学研究之间的关系。特别是，我们确认了度量分割策略 (Cutler & Norris, 1988) 的效用，并展示了一种可以让婴儿识别此效用的途径，而无需事先指定“音节”或“强音节”等类别。 ”