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摘要
This paper addresses the problem of automatic speech recognition in real applications in which the speech signal is altered by various noises Feature compensation and model compensation robustness methods are studied Parallel model combination (PMC) and its recent advances are reviewed and a novel algorithm called PC-PMC is proposed This algorithm utilizes cepstral mean subtraction (CMS) normalization ability and principal component analysis (PCA) compression and de-correlation capability in the combination with PMC model transformation method PC-PMC algorithm takes the advantages of additive noise compensation ability of PMC and convolutional noise removal capability of CMS and PCA In realizing PC-PMC mainly two problems should be solved The first problem is that PMC method requires invertible modules in the front-end of the system while CMS normalization is not an invertible process Moreover when the recognition system is exposed to noisy speech the adaptation of the PCA transform is required therefore a framework is to be designed for adaptation of the PCA transform in the presence of noise The method presented in this paper provides solutions to these problems
Our evaluations are done on the four different real noisy tasks using Nevisa HMM-based Persian continuous speech recognition system Experimental results demonstrate significant reduction in the system word error rate using PC-PMC In addition the effects of covariance matrix compensation dynamic features adaptation and the effect of gain parameter in the PMC method are also studied and experimented in the real acoustic conditions Besides we have investigated the using of maximum likelihood linear regression (MLLR) and maximum a posteriori (MAP) adaptation techniq
- 出版日期2011-1
