Combining Mllr Adaptation and Feature Extraction for Robust Speech Recognition in Reverberant Environments

This paper presents an investigation on speech recognition performance in reverberant environments. Reverberant noise has been a major concern in speech recognition systems. Many speech recognition systems, even with state-of-art features, fail to respond to reverberant effects and the recognition rate deteriorates. This shows the limitations of robust feature extraction in reverberant environment. The maximum likelihood linear regression (MLLR) adaptation scheme is adopted for reverberant speech recognition on the TI-DIGIT database. The use of adaptation data improved the recognition performance significantly especially for strong reverberations. The performance of both MFCC 0 and MFCC 0 D A features improved by more than 10% for reverberations greater than 0.4s. This paper also demonstrates the optimal strength of both robust feature extraction and adaptation scheme for reverberant speech recognition. The recognition performance is maintained above 90% up to reverberation time 0.5s using both schemes.