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）系统仍然与特定领域（“航行”或“商业新闻”）相关，并且要精心发音，言语缓慢，因为我们的统计算法并不能很好地处理发音，文字选择和语法中的极端变化，以表征自然，对话，对话，对话，对话，对话。 该职业项目的研究目标是通过在词汇处理上取得人类的心理结果，为它们提供明确的概率模型，并将其整合到ASR算法中，来建立更好的语音识别者。 该项目将词性和单词频率纳入ASR中的单词发音模型中。 该项目正在使用ASR系统中的单词含义的分布矢量模型来增加单词在相似单词附近发生的概率，从而汇编动词argument结构的概率词典来帮助ASR系统。 这项职业调查的教育目标是将计算，基于语料库和概率的培训纳入语音，心理语言学，计算语言学和认知科学的本科和研究生课程。 通过其研究和教育方面，职业项目将有助于将有才华和知识渊博的语言学家和心理学家吸引到ASR和相关领域，并直接影响ASR及其技术和商业意义。