Evaluation and Optimization of Perceptually-Based ASR Front-End

Several recently proposed automatic speech recognition (ASR) front-ends are experimentally compared in speaker-dependent, speaker-independent (or cross-speaker) recognition. The perceptually-based linear predictive (PLP) front-end, with the root-power sums (RPS) distance measure, yields generally the highest accuracies, especially in cross-speaker recognition. It is experimentally shown that we can optimize the system and further improve recognition accuracy for speaker-independent recognition by controlling the distance measure's sensitivity to spectral peaks and the spectral tilt and by utilizing the speech dynamic features. For a digit vocabulary, and five reference templates obtained with a clustering algorithm, the optimization improves recognition accuracy from 97% to 98.1%, with respect to the PLP_RPS front-end.