Unsupervised speaker adaptation method based on hierarchical spectral clustering

Abstract

An automatic speaker adaptation method is proposed for speech recognition in which a small amount of training material of unspecified text can be used. This method is easily applicable to vector-quantization-based speech recognition systems where each word is represented as multiple sequences of codebook entries. In the adaptation algorithm, either the codebook is modified for each new speaker or input speech spectra are adapted to the codebook, thereby using codebook sequences universally for all speakers. The important feature of this algorithm is that a set of spectra in training frames and the codebook entries are clustered hierarchically. Based on the deviation vectors between centroids of the training frame clusters and the corresponding codebook clusters, adaptation is performed hierarchically from small to large numbers of clusters. Results of recognition experiments indicate that the proposed adaptation method is highly effective. Possible variations using this method are presented. >