An improved parallel model combination method for noisy speech recognition

In this paper a novel method, called PC-PMC, is proposed to improve the performance of automatic speech recognition systems in noisy environments. This method is based on the parallel model combination (PMC) technique and uses the Cepstral Mean Subtraction (CMS) normalization ability and Principal Component Analysis (PCA) compression and de-correlation capabilities. It takes the advantages of both additive noise compensation of PMC and convolutive noise removal ability of CMS and PCA. The first problem to be solved in the realizing of PC-PMC is that PMC algorithm requires invertible modules in the front-end of the system while CMS normalization is not an invertible process. Also, it is required to design a framework for adaptation of the PCA transform in the presence of noise. The method proposed in this paper provides solutions to the both problems. Our evaluations are done on four different real noisy tasks using Nevisa Persian continuous speech recognition system. Experimental results demonstrate significant reduction in word error rate using PC-PMC in comparison with the standard robustness methods.