Unsupervised HMM adaptation based on speech-silence discrimination

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.