Joint Bayesian predictive classification and parallel model combination for robust speech recognition
Svein Gunnar Pettersen, Magne Hallstein Johnsen, Tor André Myrvoll · 2005
In this paper we present an approach that makes use of both Bayesian predictive classification (BPC) and parallel model combination (PMC) to achieve increased robustness towards noise. PMC provides a method for finding parameter estimates for speech corrupted by noise, while BPC is a method that com-pensates for uncertainty of parameter estimates. Thus, these methods can be combined in order to obtain knowledge about the mismatch situation and simultaneously account for uncer-tainty in this knowledge. We apply this technique in an unsu-pervised approach on the Aurora2 database and show that good performance is obtained. 1.