Wavelet-based, speaker-independent isolated Hindi digit recognition
Haamid M. Gazi, Omar Farooq, Yusuf Uzzaman Khan, Sekharjit Datta · International Journal of Information and Communication Technology · 2008
In this paper, Admissible Wavelet Packet (AWP)-based features are proposed for the recognition of isolated Hindi digit. AWPs are used to design a set of filter banks to follow the Mel scale. Finally, a Hidden Markov Model (HMM) is developed for recognition. The database used was collected from 48 subjects with a repetition of each digit five times. Features based on both Linear Predictive Coefficients (LPCs) as well as Mel Frequency Cepstral Coefficients (MFCCs) were extracted and their performance compared with the AWP-based features. It was found that the recognition performance using AWP-based features was superior when compared with LPC- and MFCC-based features.