Feature-based noise robust speech recognition on an Indonesian language automatic speech recognition system

Mel-frequency Cesptral Coefficients (MFCC) and Predictive Linear Prediction (PLP) coefficients are two popular representations of continuous speech in existing Hidden Markov Model (HMM) based Automatic Speech Recognition (ASR) systems. Cepstral Mean Normalization (CMN) is often used as a post-processing step in the extraction of MFCC and PLP features to further enhance noise robustness at almost negligible computational cost. In this paper we build a closed dictionary, large vocabulary HMM-based Indonesian language ASR system using the CMU Sphinx in speech recognition toolkit implementing MFCC and PLP feature extraction, and CMN. We test the effect of various types and levels of noise on the word error rate (WER) of speech recognition. Utilizing CMN, an average improvement of 2% recognition over standard MFCC and PLP extraction methods is obtained at signal-to-noise ratios (SNR) below 24 decibels. A significant drop in recognition is observed between 12 and 6 dB SNR.