Learning Tone

人类语言使用音高来传达信息。 这些信息的范围从具有词汇语气的语言中的词义到各种语言中的句法和语用信息。 此外，儿童语言研究表明，超清晰的音高，响度和持续时间在儿童指导的讲话中发挥作用，引导语调以及词汇和句法习得。 然而，尽管这种音调信息的根本重要性，语音识别和处理的计算方法在很大程度上将这种音调变化视为要归一化的噪声源。 该项目建立在最近的语音研究的基础上，该研究确定了上下文在音调实现和音调变化中的关键作用，并通过音调变化的最大速率和音调协同发音的机制来解释。 本研究开发了一个更广泛的背景下，发音动机的语气模型，利用一个共同的框架，在一系列的语言和语气类型，包括班图语，汉语方言，英语。 超清晰度的儿童导向的讲话被利用来识别语言相关的变化，并了解其在声调习得的作用。 通过无监督学习，这项工作自动识别自然语音中的音调和音高口音，同时高度利用稀疏的手动注释资源进行黄金标准评估。在这个项目中开发的改进的音调建模和识别技术将允许计算口语理解系统更充分地利用音高所携带的信息。 这些组件亦会把有关声调和音高重音的反馈纳入电脑辅助学习系统，以加强对语文学习的支援。

Human languages make crucial use of pitch to convey information. This information ranges from word meaning in languages with lexical tone to syntactic and pragmatic information across a wide range of languages. Furthermore, child language research has suggested that hyper-articulated pitch, loudness, and duration in child-directed speech play a role in bootstrapping intonational as well as lexical and syntactic acquisition. However, despite the fundamental importance of this tone information, computational approaches to speech recognition and processing have largely viewed such pitch variation as a source of noise to be normalized away. This project builds on recent phonetic research that identifies the key role of context in tone realization and pitch variation, explained through mechanisms of maximum rate of pitch change and tonal coarticulation. This research develops a broader-context, articulatorily-motivated model of tone, utilizing a common framework across a range of language and tone typologies including Bantu languages, Chinese dialects, and English. The hyper-articulation of child-directed speech is exploited to identify linguistically relevant variation and to understand its role in tone acquisition. Through unsupervised learning, this work automatically identifies tone and pitch accent in natural speech, while highly leveraging sparse, manually annotated resources for gold-standard evaluation. The improved techniques for modeling and recognition of tone developed in this project will allow computational spoken language understanding systems to more fully exploit the information carried by pitch. These components will also enhance support for language learning through integration of feedback on tone and pitch accent use in a computer-assisted learning system.