CAREER: Spoken Lexical Processing in Humans and Machines

职业：人类和机器的口语词汇处理

• 批准号: 9733067
• 负责人: Daniel Jurafsky
• 金额: $ 45万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-05-15 至 2003-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9733067&HistoricalAwards=false
• 关键词: CAREER; Spoken; Lexical; Processing; Humans

Our most successful Automatic Speech Recognition (ASR) systems remain too tied to specific domains (`air travel' or `business news') and to carefully pronounced, slow speech, because our statistical algorithms do not deal well with the extreme variation, in pronunciation, word-choice, and grammar, that characterizes natural, conversational, speech. The research goal of this CAREER project is to build better speech recognizers by taking human psychological results on lexical processing, giving them explicit probabilistic models, and integrating them into ASR algorithms. This project is incorporating part-of-speech and word frequency into probabilistic models of word pronunciation in ASR. This project is using distributional-vector models of word meaning in an ASR system to increase the probability of a word occurring near similar words, compiling probabilistic dictionaries of verb-argument structure to help ASR systems. The educational goal of this CAREER investigation is to incorporate computational, corpus-based, and probabilistic training into the undergraduate and graduate curriculum in phonetics, psycholinguistics, computational linguistics, and cognitive science. Through its research and educational facets, the CAREER project will help draw talented and knowledgeable linguists and psychologists into ASR and related fields, and produce research with a direct impact on ASR and its technical and commercial implications.

我们最成功的自动语音识别（ASR）系统仍然过于依赖于特定的领域（“航空旅行”或“商业新闻”）和仔细发音，缓慢的语音，因为我们的统计算法不能很好地处理 发音、用词和语法的极端变化，是自然的、会话的语言的特征。 这个CAREER项目的研究目标是通过对词汇处理的人类心理结果，为它们提供明确的概率模型，并将它们集成到ASR算法中，来构建更好的语音识别器。 该项目将词性和词频纳入ASR中单词发音的概率模型。 这个项目是在ASR系统中使用词义的分布向量模型来增加单词出现的概率 附近 类似 文字， 编译 动词论元结构的概率词典，以帮助ASR系统。 这项CAREER调查的教育目标是将计算，基于语料库和概率的培训纳入大学生 和 研究生 课程 在 语音学，心理语言学，计算语言学， 和 认知科学 通过其研究和教育方面，CAREER项目将有助于吸引有才华和知识渊博的语言学家和心理学家进入ASR和相关领域，并产生对ASR及其技术和商业影响直接影响的研究。