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Speech-silence discrimination based on unsupervised HMM adaptation

机译:基于无监督HMM自适应的语音沉默识别

摘要

An unsupervised, discriminative, sentence level, HMM adaptation based on speech-silence classification is presented. Silence and speech regions are determined either using a speech end-pointer or the segmentation obtained from the recognizer in a first pass. The discriminative training procedure using a GPD or any other discriminative training algorithm, employed in conjunction with the HMM-based recognizer, is then used to increase the discrimination between silence and speech.
机译:提出了一种基于语音沉默分类的无监督,判别式,句子级,HMM自适应。使用语音端点或在第一遍中从识别器获得的分段来确定沉默和语音区域。然后,使用与基于HMM的识别器结合使用的,使用GPD或任何其他判别训练算法的判别训练程序,以增加沉默和语音之间的区别。

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