Signal bias removal by maximum likelihood estimation for robust telephone speech recognition

An acoustical mismatch between the training and the testing conditions of hidden Markov model (HMM)-based speech recognition systems often causes a severe degradation in the recognition performance. In telephone speech recognition, for example, undesirable signal components due to ambient noise and channel distortion, as well as due to different variations of telephone handsets render the recognizer unusable for real-world applications, This paper presents a signal bias removal (SBR) method based on maximum likelihood estimation for the minimization of these undesirable effects, The proposed method is readily applicable in various architectures, i.e., discrete (vector-quantization based), semicontinuous and continuous density HMM, In this paper, the SBR method, integrated into a discrete density HMM, is applied to telephone speech recognition where the contamination due to extraneous signal components is assumed to be unknown, To enable real-time implementation, a sequential method for the estimation of the bias is presented, Experimental results for speaker-independent connected digit recognition show a reduction in the per digit error rate by up to 41% and 14% during mismatched and matched training and testing conditions, respectively.