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 nonstationary noises compared to the most popular baseline noise tracking algorithm MCRA and its derivatives.
Bibliographic reference. Chowdhury, Md Foezur Rahman / Selouani, Sid-Ahmed / O'Shaughnessy, Douglas (2011): "A rapid adaptation algorithm for tracking highly non-stationary noises based on Bayesian inference for on-line spectral change point detection", In INTERSPEECH-2011, 1205-1208.