Robust Perceptual Wavelet Packet Features for Recognition of Continuous Kannada Speech

Abstract

An ASR system is built for the Continuous Kannada Speech Recognition. The acoustic and language models are created with the help of the Kaldi toolkit. The speech database is created with the native male and female Kannada speakers. The 75% of collected speech data is used for training the acoustic models and 25% of speech database is used for the system testing. The Performance of the system is presented interms of Word Error Rate (WER). Wavelet Packet Decomposition along with Mel filter bank is used to achieve feature extraction. The proposed feature extraction performs slightly better than the conventional features such as MFCC, PLP interms of WRA and WER under uncontrolled conditions. For the speech corpus collected in Kannada Language, theproposed features shows an improvement in WRA of 1.79% over baseline features.