A highly non-stationary noise tracking and compensation algorithm, with applications to speech enhancement and on-line ASR

This paper presents a noise tracking and estimation algorithm for highly non-stationary noises using the Bayesian on-line spectral change point detection (BOSCPD) technique. In BOSCPD, the local minima search window update technique of minima controlled recursive averaging (MCRA) algorithm is made a function of spectral change point detection. The novelty of this algorithm is that it can detect the rapid changes instantly and quickly update the non-stationary noise estimate compared to the MCRA-based algorithms. The BOSCPD algorithm shows improvement in objective quality measures in terms of higher SNR and lower output distortion scores for speech enhancement. It is also tested to track and compensate for rapidly varying noises in on-line automatic speech recognition (ASR) using the Aurora 2 speech database. The simulation results show significant improvement in recognition accuracy compared to the baseline MCRA technique.