Energy conditioned spectral estimation for recognition of noisy speech

An estimation algorithm to improve the noise robustness of filterbank-based speech recognition systems is presented. The algorithm is based on a minimum mean square error (MMSE) estimation of the filter log-energies, introducing a significant improvement over related published algorithms by conditioning the estimate on the total frame energy. The algorithm was evaluated with DECIPHER, SRI's continuous-speech speaker-independent recognizer, on two types of noisy speech: a standard database with added white Gaussian noise, and recordings made in a noisy environment. With white noise the recognition accuracy obtained while training on clean speech and testing in noise approached that obtained with training and testing in noise. In the noisy environment, the estimation algorithm boosted the recognition system's performance with a table mounted microphone almost to the level achieved with a close talking microphone.<
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