A Rapid Adaptation Algorithm for Tracking Highly Non-Stationary Noises based on Bayesian Inference for On-Line Spectral Change Point Detection

This paper presents an innovative rapid adaptation technique for tracking highly non-stationary acoustic noises. The novelty of this technique is that it can detect the acoustic change points from the spectral characteristics of the observed speech signal in rapidly changing non-stationary acoustic environments. The proposed innovative noise tracking technique will be very suitable for joint additive and channel distortions compensation (JAC) for on-line automatic speech recognition (ASR). The Bayesian on-line change point detection (BOCPD) approach is used to implement this technique. The proposed algorithm is tested using highly non-stationary noisy speech samples from the Aurora2 speech database. Significant improvement in minimizing the delay in adaptation to new acoustic conditions is obtained for highly non-stationary noises compared to the most popular baseline noise tracking algorithmMCRA and its derivatives.