Learning Tone

人类语言使用音调来传达信息是至关重要的。这些信息的范围从具有词汇语调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，儿童语言中的超清音调、音量和时长在引导语调以及词汇和句法习得方面发挥了作用。然而，尽管这种音调信息很重要，但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需要归一化的噪声源。这个项目建立在最近的语音研究的基础上，该研究确定了语境在音调实现和音调变化中的关键作用，并通过最大音调变化率和音调协同发音机制进行了解释。这项研究开发了一个更广泛的语境、发音理据的声调模式，利用了一个共同的框架，涵盖了包括班图语、汉语方言和英语在内的一系列语言和语调类型。儿童导向言语的超清晰度被用来识别与语言相关的变异，并理解其在声调习得中的作用。通过无监督学习，这项工作自动识别自然语音中的语调和基音重音，同时高度利用稀疏的、手动注释的资源进行黄金标准评估。本项目开发的改进的声调建模和识别技术将使计算机口语理解系统能够更充分地利用基音携带的信息。这些构成部分还将通过将关于语调和音调使用的反馈整合到计算机辅助学习系统中，加强对语言学习的支持。

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.