Perceptually Motivated Linear Prediction Cepstral Features for Network Speech Recognition

Abstract

In this paper, we propose a new method for modifying the power spectrum of input speech to obtain a set of perceptually motivated Linear Prediction (LP) parameters that provide noise-robustness to Automatic Speech Recognition (ASR) features. Experiments were performed to compare the recognition accuracy obtained from Perceptual Linear Prediction-Cepstral Coefficients (PLP-LPCCs) and cepstral features derived from the conventional Linear Prediction Coding (LPC) parameters with that obtained from the proposed method. The results show that, using the proposed approach, the speech recognition performance was on average 4.93% to 7.09% and 3% to 5.71% better than the conventional method and the PLP-LPCCs, respectively, depending on the recognition task.