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首页> 美国政府科技报告 >Robust Speech Recognition Using Hidden Markov Models: Overview of a Research Program.
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Robust Speech Recognition Using Hidden Markov Models: Overview of a Research Program.

机译：使用隐马尔可夫模型的鲁棒语音识别：研究计划概述。

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This report presents an overview of a program of speech recognition research which was initiated in 1985 with the major goal of developing techniques for robust high performance speech recognition under the stress and noise conditions typical of a military aircraft cockpit. The work on recognition in stress and noise during 1985 and 1986 produced a robust Hidden Markov Model (HMM) isolated-word recognition (IWR) system with 99 percent speaker-dependent accuracy for several difficult stress/noise data bases, and very high performance for normal speech. Robustness techniques which were developed and applied include multi-style training, robust estimation of parameter variances, perceptually-motivated stress-tolerant distance measures, use of time-differential speech parameters, and discriminant analysis. These techniques and others produced more than an order-of-magnitude reduction in isolated-work recognition error rate relative to a baseline HMM system. An important feature of the Lincoln HMM system has been the use of continuous-observation HMM techniques, which provide a good basis for the development of the robustness techniques, and avoid the need for a vector quantizer at the input to the HMM system. Beginning in 1987, the robust HMM system has been extended to continuous speech recognition for both speaker-dependent and speaker-independent tasks. The robust HMM continuous speech recognizer was integrated in real-time with a stressing simulated flight task, which was judged to be very realistic by a number of military pilots. (kr)

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