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.

该项目的目标是开发一个建模框架，以便在需要口语理解的一大批应用程序中广泛使用韵律信息，例如音调、持续时间和能量特征。为此，从自动可检测事件定义的区域上的语音信号中提取韵律特征。结果是通常高维向量的可变长度序列，具有混合的离散和连续分布以及未定义的值。该项目的重点是寻找一种变换，当应用于韵律特征时，会产生一个可以充分表示原始韵律特征序列的重要特征的向量。所提出的变换是通过将特定样本中的特征分布投影到预先确定的字典中由动态贝叶斯网络表示的一组概率分布来形成的。该项目的最终目标是创建基于通用概率模型的变换范式可以对复杂的功能集发挥强大的作用。因此，这项工作也将为其他领域做出贡献，这些领域的特征表现出对标准方法具有挑战性的特征。项目期间开发的工具和语料库将向社区提供。该项目的结果将有助于增进有关韵律信息使用的科学知识，并提高口语理解和对话系统的能力。