Filterbank-energy estimation using mixture and Markov models for recognition of noisy speech

An estimation algorithm for noise robust speech recognition, the minimum mean log spectral distance (MMLSD), is presented. The estimation is matched to the recognizer by seeking to minimize the average distortion as measured by a Euclidean distance between filterbank log-energy vectors, approximating the weighted-cepstral distance used by the recognizer. The estimation is computed using a clean speech spectral probability distribution, estimated from a database, and a stationary, ARMA model for the noise. When trained on clean speech and tested with additive white noise at 10-dB SNR, the recognition accuracy with the MMLSD algorithm is comparable to that achieved with training the recognizer at the same constant 10-dB SNR. The algorithm is also highly efficient with a quasi-stationary environmental noise, recorded with a desktop microphone, and requires almost no tuning to differences between this noise and the computer-generated white noise