Estimation using log-spectral-distance criterion for noise-robust speech recognition

A spectral-estimation algorithm designed to improve the noise robustness of speech-recognition systems is presented and evaluated. The algorithm is tailored for filter-bank-based systems, where the estimation seeks to minimize the distortion as measured by the recognizer´s distance metric. This minimization is achieved by modeling the speech distribution as consisting of clusters; the energies at different frequency channels are assumed to be uncorrelated within each cluster. The algorithm was tested with a continuous-speech, speaker-independent hidden Markov model (HMM) recognition system using the NIST Resource Management Task speech database. When trained on a clean speech database and tested with additive white Gaussian noise, the recognition accuracy with the new algorithm is comparable to that under the ideal condition of training and testing at constant SNR. When trained on clean speech and tested with a desktop microphone in a noisy environment, the error rate is only slightly higher than that with a close-talking microphone