Region-based vocal tract length normalization for ASR
Michail G. Maragakis, Alexandros Potamianos · 2008
In this paper, we propose a Region-based multi-parametric Vocal Tract Length Normalization (R-VTLN) algorithm for the problem of automatic speech recognition (ASR). The proposed algorithm extends the well-established mono-parametric utterance-based VTLN algorithm of Lee and Rose [1] by dividing the speech frames of a test utterance into regions and by warping independently the features corresponding to each region using a maximum likelihood criterion. We propose two algorithms for classifying frames into regions: (i) an unsupervised clustering algorithm based on spectral distance, and (ii) an unsupervised algorithm assigning frames to regions based on phonetic-class labels obtained from the first recognition pass. We also investigate the ability of various mono-parametric and multiparametric warping functions to reduce the spectral distance between two speakers, as a function of phone. R-VTLN is shown to significantly outperform mono-parametric VTLN in terms of word accuracy for the AURORA4 database.