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首页> 外文会议>International Conference on Spoken Language Processing; 20041004-08; Jeju(KR) >FEATURE-DEPENDENT COMPENSATION IN SPEECH RECOGNITION
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FEATURE-DEPENDENT COMPENSATION IN SPEECH RECOGNITION

机译：语音识别中的功能依赖补偿

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摘要

Several mismatch conditions can be modeled as an additive bias. This bias is considered independent of the observation vectors, although this approximation is not always accurate. In this paper the dependence of the bias on the observation vectors is taken into consideration in the context of compensating the GSM coding distortion in speech recognition. However, the results presented here can easily be generalized to deal with other types of mismatch. The coding-decoding distortion is modeled here as feature-dependent. This model is employed to propose an Expectation-Maximization (EM) estimation algorithm of the coding-decoding distortion that is able to cancel the effect of GSM coder with as few as one adapting utterance. Finally, the feature-dependent adaptation can give word error rate (WER) 26% lower than the feature-independent model.
机译：可以将几种失配条件建模为加性偏差。尽管该近似值并不总是准确的，但认为该偏差与观察向量无关。在本文中，在补偿语音识别中的GSM编码失真的情况下，考虑了偏差对观察向量的依赖性。但是，这里介绍的结果可以轻松地推广到其他类型的不匹配。此处将编解码失真建模为与特征相关的模型。该模型用于提出一种编码-解码失真的期望最大化（EM）估计算法，该算法能够以最少的一种自适应发音消除GSM编码器的影响。最后，与特征无关的自适应模型可以使单词错误率（WER）降低26％。

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