Signal bias removal for robust telephone based speech recognition in adverse environments

A speech signal transmitted through a telephone channel often encounters variable conditions which significantly deteriorate the performance of state-of-the-art HMM-based speech recognition systems. Undesirable components due to ambient noise and channel interference, as well as different sound pick-up equipments, render the recognizer unsuitable for real-world applications. This paper presents a signal bias removal method based on the maximum likelihood estimation for the minimization of these undesirable effects. The proposed method, integrated into a discrete density HMM, is applied to telephone speech recognition where the contamination due to ambient noise and channel distortion are assumed to be unknown. Experimental results are presented for speaker-independent connected digit recognition which demonstrate a reduction in the word error rate by up to 40%