Cepstrum-domain model combination based on decomposition of speech and noise for noisy speech recognition

We propose a cepstrum-domain model combination method for automatic speech recognition in noisy environments. The distinguishing aspect of the method is that noise-corrupted speech is decomposed into clean speech and noise components directly in the cepstrum domain without having to transform to the linear spectrum domain as is necessary for many existing model combination approaches. This is accomplished by exploiting the properties of the minimum mean squared error-log spectral amplitude (MMSE-LSA) based speech enhancement algorithm. As a result, a clean speech hidden Markov model (HMM) is easily compensated for a noise-corrupted domain by adding the means and covariance matrices of the clean speech HMM and those of an estimated noise model. The complexity of the proposed model combination procedure is significantly reduced with respect to conventional parallel model combination. The procedure was applied to a noisy connected digit recognition task. A 40% reduction in word error rate was achieved when it was combined with acoustic feature compensation techniques under mismatched environmental and channel conditions.