On the Interaction Between Speaker Normalization, Environment Compensation, and Discriminant Feature Space Transformations

This paper presents a study of the interaction between frequency warping based speaker normalization algorithms, environment compensation algorithms, and discriminant feature space transformations (DFT) in providing consistent reductions in ASR word error rate (WER) over a range of acoustic degradations. Performance improvements obtained using speaker normalization algorithms, including vocal tract length normalization (VTLN) and a newly proposed augmented state space acoustic decoder, are shown to improve substantially when applied in a discriminant feature space where acoustic environment compensation has been applied. Furthermore, the effects on ASR performance of the DFT are also shown to be enhanced by reducing within class variability by applying the DFT on a speaker and an environment normalized feature space