new robust subband cepstral feature for isolated world recognition
In this paper a new robust feature extraction method for speech recognition, has been proposed. The features are obtained from Cepstral Mean Normalized reduced order Linear Predictive Coding (LPC) coefficients 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 wavelet decomposed subbands of speech frame provide better representation than modeling the frame directly. Experimentally it has been shown that, the proposed approach provides effective (better recognition rate), efficient (reduced feature vector dimension) and robust features. The speech recognition system using the Continuous Density Hidden Markov Model (CDHMM) has been implemented. The proposed algorithm is evaluated using NIST TI-46 isolated-word database.
feature extraction cepstral mean normalization linear predictive coding discrete wavelet transform