Approximated Parallel Model Combination for efficient noise-robust speech recognition
Khe Chai Sim · 2013
Parallel Model Combination (PMC) and Vector Taylor Series (VTS) are two model-based approaches for noise-robust speech recognition. The latter is more popular because of its simple compensation formulae for both the static and dynamic parameters. Furthermore, this VTS compensation formulation can be easily extended to noise adaptive training where the parameters of the underlying pseudo-clean speech and distortion models can be optimized. PMC lacks the above benefits because of its nonlinear variance compensation formula. In this paper, the Approximated PMC (APMC) method is proposed where linearized PMC variance compensation is used. The same approximation has also been applied to Trajectory-based APMC (TAPMC) to achieve a four-time computational saving over the Trajectory-based PMC (TPMC). The dynamic parameter compensation and noise re-estimation formulae for APMC are also derived. Experimental results on AURORA 4 show that APMC and TAPMC consistently outperformed the standard VTS and Trajectory-based VTS (TVTS) by 6.3% and 5.3% relative respectively.