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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
