A Bayesian Approach for Noise Suppression of Speech Signal in Real Environment
Numerous noise suppression methods for speech signals have been developed up to now. In this paper, a new method to suppress noise in speech signals is proposed, which requires a single microphone only and doesn't need any priori information on both noise spectrum and pitch. It works in the presence of noise with high amplitude. More specifically, an adaptive noise suppression algorithm applicable to real-life speech recognition is proposed without assuming the Gaussian white noise, which performs effectively even though the noise statistics and the fluctuation form of speech signal are unknown. The effectiveness of the proposed method is confirmed by applying it to real speech signals contaminated by noises.