New feature extraction methods using DWT and LPC for isolated word recognition

Abstract

In this paper a new feature extraction methods, which utilize reduced order Linear Predictive Coding (LPC) coefficients for speech recognition, have been proposed. The coefficients have been derived from the speech frames decomposed using Discrete Wavelet Transform (DWT). In the literature it is assumed that the speech frame of size 10 msec to 30 msec is stationary, however, in practice different parts of the speech signal may convey different amount of information (hence may not be perfectly stationary). LPC coefficients derived from subband decomposition of speech frame provide better representation than modeling the frame directly. Experimentally it has been shown that, the proposed approaches provide effective (better recognition rate) and efficient (reduced feature vector dimension) features. The speech recognition system using the continuous Hidden Markov Model (HMM) has been implemented. The proposed algorithms are evaluated using NIST TI-46 isolated-word database.