Modeling the Development of Phonetic Representations

听者在感知言语时所依赖的线索或感知维度上存在跨语言差异。例如，日本听众对英语[l]和[r]进行分类，并不依赖于与英语母语听众相同的语音信号声学特征（例如，第三共振峰）。这些跨语言差异通常归因于听者对声音类别的认识。例如，英语听众知道[l]和[r]是两个类别，而日语听众知道[l]和[r]是同一类别的一部分；这种认知被假设影响了他们对维度的依赖。本研究验证了类别知识不是感知维度学习的必要条件的假设。利用在低资源自动语音识别中表现良好的表示学习方法，在没有大量标记训练数据的情况下，提出了两种不依赖于声音类别知识学习维度的模型。第一种依赖于时间信息作为类别知识的代理，而第二种依赖于类似单词的自上而下的信息，婴儿已经被证明会使用这些信息。当这些模型与听者在相同的语言背景下训练时，通过母语和非母语对比的言语感知实验来预测听者的歧视判断的能力。

Listeners differ cross-linguistically in the cues, or perceptual dimensions, they rely on when perceiving speech. For example, Japanese listeners categorizing English [l] and [r] do not rely on the same acoustic features of the speech signal (e.g., the third formant) that native English listeners do. These cross-linguistic differences are typically attributed to listeners' knowledge of sound categories. For example, English listeners know that [l] and [r] are two categories, whereas Japanese listeners know that [l] and [r] are part of the same category; and this knowledge is hypothesized to affect their reliance on dimensions.The proposed research tests the hypothesis that category knowledge is not necessary for perceptual dimension learning to occur. Drawing on representation learning methods that have performed well in low-resource automatic speech recognition, where extensive labeled training data are not available, two models are proposed that learn dimensions without relying on knowledge of sound categories. The first relies on temporal information as a proxy for category knowledge, while the second relies on top-down information from similar words, which infants have been shown to use. These models are evaluated on their ability to predict listeners' discrimination judgments from speech perception experiments on native and non-native contrasts, when trained on the same language background as the listeners.