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首页> 外文期刊>Signal Processing Magazine, IEEE >Large-Vocabulary Continuous Speech Recognition Systems: A Look at Some Recent Advances
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Large-Vocabulary Continuous Speech Recognition Systems: A Look at Some Recent Advances

机译：大词汇量连续语音识别系统：最近的一些进展

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摘要

Over the past decade or so, several advances have been made to the design of modern large vocabulary continuous speech recognition (LVCSR) systems to the point where their application has broadened from early speaker dependent dictation systems to speaker-independent automatic broadcast news transcription and indexing, lectures and meetings transcription, conversational telephone speech transcription, open-domain voice search, medical and legal speech recognition, and call center applications, to name a few. The commercial success of these systems is an impressive testimony to how far research in LVCSR has come, and the aim of this article is to describe some of the technological underpinnings of modern systems. It must be said, however, that, despite the commercial success and widespread adoption, the problem of large-vocabulary speech recognition is far from being solved: background noise, channel distortions, foreign accents, casual and disfluent speech, or unexpected topic change can cause automated systems to make egregious recognition errors. This is because current LVCSR systems are not robust to mismatched training and test conditions and cannot handle context as well as human listeners despite being trained on thousands of hours of speech and billions of words of text.
机译：在过去的十年左右的时间里，现代大型词汇连续语音识别（LVCSR）系统的设计取得了一些进展，其应用范围已从早期的依赖于说话者的听写系统扩展到了与说话者无关的自动广播新闻转录和索引编制，演讲和会议转录，对话电话语音转录，开放域语音搜索，医学和法律语音识别以及呼叫中心应用等。这些系统的商业成功证明了LVCSR的研究已经取得了多大的成就，本文的目的是描述现代系统的一些技术基础。但是，必须说，尽管在商业上取得了成功并得到了广泛的采用，但大词汇量语音识别的问题仍远未解决：背景噪音，通道失真，外来口音，随意和流利的语音或意外的主题更改都可以解决。导致自动化系统犯下严重的识别错误。这是因为当前的LVCSR系统对不匹配的训练和测试条件不具有鲁棒性，并且尽管接受了数千小时的语音和数十亿个文字的训练，但仍无法处理环境以及听众。

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