STIMULATE: Exploiting Nonlocal and Syntactic Word Relationships in Language Models for Conversational Speech Recognition

刺激：利用语言模型中的非局部和句法词关系进行会话语音识别

• 批准号: 9618874
• 负责人: Frederick Jelinek
• 金额: $ 75万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-03-01 至 2001-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9618874&HistoricalAwards=false
• 关键词: STIMULATE; Exploiting; Nonlocal; Syntactic; Word

Automated systems that can interact with human users in modalities such as speech and handwriting will greatly enhance productivity and system usability. These systems will allow simple access to information and services on the internet. These capabilities are essential to other tasks, such as enabling access by handicapped users or querying an on-line maintenance manual while performing intricate repairs. A statistical model of language is a crucial component in such systems, which convert between speech or handwriting and text, and in statistical machine translation systems. Most current algorithms for language modeling exhibit an acute myopia, basing their predictions of the next word on only a few immediately preceding words. When humans are faced with a comparable task they easily outperform these models using the richer linguistic information available to them from more complete context. Researchers at CLSP propose to investigate and develop novel language modeling techniques that exploit richer contextual information. They propose to examine models that use a variety of techniques to capture syntactic dependencies through dynamic, hierarchical models of topic, and to combine the resulting models with the best current ones using the maximum entropy principle. This research will focus on improving the recognition accuracy of spontaneous human speech, but beyond that will provide insight into new information sources and techniques applicable to all applications of language modeling.

能够以语音和手写等方式与人类用户交互的自动化系统将极大地提高生产率和系统可用性。这些系统将允许简单地访问互联网上的信息和服务。这些功能对于其他任务是必不可少的，例如使残障用户能够访问或在执行复杂的维修时查询在线维护手册。在这样的系统中，语言的统计模型是至关重要的组成部分，这些系统在语音或手写和文本之间进行转换，在统计机器翻译系统中也是如此。大多数当前的语言建模算法表现出严重的短视，他们对下一个单词的预测只基于紧随其后的几个单词。当人类面临类似的任务时，他们会使用更完整的语境中提供的更丰富的语言信息，轻松地超越这些模型。CLSP的研究人员建议研究和开发新的语言建模技术，利用更丰富的语境信息。他们建议检查那些使用各种技术通过动态的、分层的主题模型来捕获句法依赖的模型，并使用最大熵原则将结果模型与最好的当前模型结合起来。这项研究将专注于提高人类自发语音的识别精度，但除此之外，还将为适用于语言建模所有应用的新信息源和技术提供见解。