Nonlinear resampling transformation for automatic speech recognition

Abstract

A new technique for speech signal processing called nonlinear resampling transformation (NRT) is proposed. The representation of a speech pattern derived from this technique has two important features: first, it reduces redundancy; second, it effectively removes the nonlinear variations of speech signals in time. The authors have applied NRT to the TI isolated-word database achieving a 99.66% recognition rate on a 10 digits multi-speaker task for a linear predictive neural net classifier. In their experiment, the authors have also found that discriminative training is superior to nondiscriminative training for linear predictive neural network classifiers.<>