CAREER: Spoken Lexical Processing in Humans and Machines

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”）和仔细发音的慢速语音，因为我们的统计算法不能很好地处理语音、单词选择和语法的极端变化，而这些变化是自然的、对话式的语音的特征。CAREER项目的研究目标是将人类在词汇处理方面的心理结果，给出明确的概率模型，并将其集成到ASR算法中，从而构建更好的语音识别器。这个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系统中使用词意的分布向量模型来增加一个词在相似词附近出现的概率，编译动词参数结构的概率字典来帮助ASR系统。这项CAREER调查的教育目标是将计算、基于语料库和概率训练纳入语音学、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，CAREER项目将帮助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及其技术和商业意义有直接影响的研究。

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.