Noisy speech recognition using hidden Markov model state-based filtering

A system which exploits this property of hidden Markov models (HMM) in order to implement effective noise canceling filters within the speech recognition task is described. The filtering process reduces the sensitivity of the recognition system to input fluctuations caused by noise. The system developed uses continuous probability density function, single Gaussian mixture HMMs trained on filterback output vectors. Using autocorrelation statistics collected for speech and noise during training, noise canceling Wiener filters are designed for each hidden Markov model state. The resulting system outperforms by a significant margin results obtained using clean-speech HMMs on either noisy speech or noisy speech with the noise mean subtracted.<
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