An algorithm of Model Compensation based on the estimation of additive noise and channel function for speech recognition

Speech recognition systems declined roughly in performance when they were facing the impacts of various additive noise and channel distortions in the actual environment,so it was of great significance for the speech recognition system to alleviate these impacts of the noise and distortions.An algorithm of model compensation was proposed, which computed the additive noise from the non-speech segments of the sentence,estimated the channel function using the EM algorithm,and jointly compensated the mismatched acoustics HMM models in the cepstral domain with them. Experiments employing this algorithm showed the significant improvement more than 50 percent relatively.The algorithm tracked the changes in the environment dynamically and it provided better performance than the traditional robust speech recognition algorithms.