Collaborative Research: Global Measures of Tonal Alignment in a Level-based Theory of Intonational Phonology

合作研究：基于水平的语调音韵学理论中音调对齐的全局测量

• 批准号: 0842782
• 负责人: Stefanie Shattuck-Hufnagel
• 金额: $ 3.28万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0842782&HistoricalAwards=false
• 关键词: Collaborative; Research; Global; Measures; Tonal

Human languages use pitch to convey meaning in a bewildering variety of ways. In all languages, pitch (as one aspect of speech prosody) can express attitude or emotion. In some languages, like English, pitch patterns, usually called intonation contours, also express distinctions such as that between a question and a statement. In languages like Mandarin Chinese, on the other hand, pitch patterns called tones go still further to signal differences between words that are otherwise identical. Despite significant advances in recent decades, a unified theoretical account of the linguistic phenomena called tone and intonation remains elusive in crucial respects. In fact, there is still no universal agreement even over how best to characterize the formal properties of intonation contours in languages like English. What seems to be missing is a common acoustic or articulatory vocabulary for the expression of the relevant distinctions, a single measurable dimension within which contours can be reliably distinguished regardless of the language under investigation. This project develops a new mathematical approach, based on the notion of Tonal Center of Gravity, to characterizing pitch distinctions in English. This approach reconciles seemingly contradictory results from the experimental literature on the perception of contrasting pitch contours. Our research will concentrate on a small number of intonation contours from English, and will test the applicability of our model in two kinds of experiments. The first involves automatic classification of pitch contours recorded from native speakers in an experimental setting. The second involves direct manipulation through speech synthesis of a number of the physical characteristics of pitch contours that might be involved in conveying speakers' intentions, to see which variables have the greatest effect on listeners' judgments of utterance meaning.The project lays the groundwork for a unified approach to the production and perception of tonal contrasts in human language, one that expresses how systems differ, while capturing the deeper commonality among languages at the same time. The results of this work will also have a broader impact in the area of speech technology. For example, identifying the perceptually relevant characteristics of distinct intonation patterns will allow for greater realism in speech synthesis systems. In addition, being able to distinguish automatically between similar but perceptually distinct contours will allow automatic pronunciation training systems to give more helpful feedback to second language learners. Finally, this new model will facilitate automatic categorization of intonation contours for speech understanding systems. Progress in this area will ultimately allow such systems to derive meaning not just from the sequence of words in an utterance, but from the subtle meaning differences conveyed by intonation as well.

人类语言使用音高以各种各样令人困惑的方式传达意思。在所有的语言中，音高（作为语音韵律的一个方面）都可以表达态度或情感。在某些语言中，如英语，音高模式，通常称为语调轮廓，也表达了诸如问题和陈述之间的区别。另一方面，在像汉语普通话这样的语言中，被称为音调的音高模式更进一步地表明了词与词之间的差异，这些词在其他方面是相同的。尽管近几十年来取得了重大进展，但在一些关键方面，对被称为语气和语调的语言现象的统一理论解释仍然难以捉摸。事实上，甚至在如何最好地描述像英语这样的语言中语调轮廓的形式属性方面，仍然没有普遍的共识。似乎缺少的是一个共同的声学或发音词汇来表达相关的区别，一个单一的可测量的维度，在这个维度内，轮廓可以可靠地区分，而不管所研究的语言是什么。本研究以声调重心的概念为基础，发展出一种新的数学方法，来描述英语中的音高差异。这种方法调和看似矛盾的结果，从实验文献的感知对比音高轮廓。我们的研究将集中在少量的英语语调轮廓，并将测试我们的模型的适用性，在两种实验。第一个涉及自动分类的音高轮廓记录从母语人士在实验设置。第二个是通过语音合成直接操纵音调轮廓的一些物理特征，这些特征可能与表达说话者的意图有关，以了解哪些变量对听者对话语意义的判断影响最大。该项目为人类语言中音调对比的产生和感知奠定了基础，同时捕捉语言之间更深层次的共性。这项工作的结果也将在语音技术领域产生更广泛的影响。例如，识别不同语调模式的感知相关特性将允许语音合成系统中的更大真实性。此外，能够自动区分相似但感知上不同的轮廓将允许自动发音训练系统为第二语言学习者提供更有帮助的反馈。最后，这个新的模型将有助于语音理解系统的语调轮廓的自动分类。这一领域的进展最终将使这类系统不仅能从话语中的单词序列中获得意义，还能从语调所传达的微妙意义差异中获得意义。