A novel feature transformation for vocal tract length normalization in automatic speech recognition

Abstract

This paper proposes a method to transform acoustic models that have been trained with a certain group of speakers for use on different speech in hidden Markov model based (HMM-based) automatic speech recognition. Features are transformed on the basis of assumptions regarding the difference in vocal tract length between the groups of speakers. First, the vocal tract length (VTL) of these groups has been estimated based on the average third formant F/sub 3/. Second, the linear acoustic theory of speech production has been applied to warp the spectral characteristics of the existing models so as to match the incoming speech. The mapping is composed of subsequent nonlinear submappings. By locally linearizing it and comparing results in the output, a linear approximation for the exact mapping was obtained which is accurate as long as the warping is reasonably small. The feature vector, which is computed from a speech frame, consists of the mel scale cepstral coefficients (MFCC) along with delta and delta/sup 2/-cepstra as well as delta and delta/sup 2/ energy. The method has been tested for TI digits data base, containing adult and children speech, consisting of isolated digits and digit strings of different length. The word error rate when trained on adults and tested on children with transformed adult models is decreased by more than a factor of two compared to the nontransformed case.