Spectral Estimation for Noise Robust Speech Recognition

We present results on the recognition accuracy of a continuous speech, speaker independent HMM recognition system that incorporates a novel noise reduction algorithm. The algorithm is a minimum mean square error estimation tailored for a filter-bank front-end. It introduces a significant improvement over similar published algorithms by incorporating a better statistical model for the filter-bank log-energies, and by attempting to jointly estimate the log-energies vector rather than individual components. The algonthm was tested with SRrs recognizer trained on the official speaker-independent "Resource management task" clean speech database. When tested with additive white gaussian noise, the noise reduction achieved by the algorithm is equivalent to a 13 dB SNR improvement. When tested with desktop microphone recordings, the error rate at 13 dB SNR is only 40% higher than that with close-talking microphone at 31 dB SNR.