RI: Statistical Modeling of Prosodic Features in Speech Technology

RI：语音技术中韵律特征的统计建模

• 批准号: 0710833
• 负责人: Robert Gray
• 金额: $ 19.91万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0710833&HistoricalAwards=false
• 关键词: RI; Statistical; Modeling; Prosodic; Features

The objective of this project is to develop a modeling framework in order to enable extensive use of prosodic information, such as pitch, duration and energy characteristics, in a large class of applications that call for spoken language understanding. For this purpose, prosodic features are extracted from the speech signal over regions defined by automatically detectable events. The result is a variable-length sequence of usually high-dimensional vectors, with mixed discrete and continuous distributions and undefined values. The focus of the project is the search for a transformation that, when applied to the prosodic features, results in a single vector that can adequately represent the important characteristics of the original sequence of prosodic features. The proposed transform is formed by projecting the distribution of the features in a certain sample onto a set of probability distributions represented by dynamic Bayesian networks in a predetermined dictionary.The ultimate goal of the project is the creation of a general probabilistic model-based transform paradigm that can act robustly on complex feature sets. This work will therefore also contribute to other domains where features exhibit characteristics that are challenging for standard approaches. The tools and corpora developed during the project will be made available to the community. The results from this project will contribute to scientific knowledge on the use of prosodic information and increase the capabilities of spoken language understanding and dialog systems.

该项目的目的是开发一个建模框架，以便在需要口头语言理解的大量应用中广泛使用韵律信息，例如音高，持续时间和能量特征。为此，从自动检测到的事件定义的区域中，从语音信号中提取了韵律特征。结果是通常高维矢量的可变长度序列，具有混合离散和连续分布以及未定义的值。该项目的重点是搜索转换，该转换应用于韵律特征时会导致单个向量，该向量可以充分代表韵律特征原始序列的重要特征。所提出的转换是通过将特定样本中特征的分布投影到预定词典中以动态贝叶斯网络表示的一组概率分布形成的。因此，这项工作还将有助于其他特征表现出对标准方法具有挑战性的特征的领域。该项目期间开发的工具和语料库将提供给社区。该项目的结果将有助于有关使用韵律信息的科学知识，并提高口语理解和对话系统的能力。