Improved parallel model combination techniques with split Gaussian mixtures for speech recognition under noisy conditions

摘要

The parallel model combination (PMC) technique has been very successful and frequently used to improve the performance of a speech recognition system under noisy environments. In this approach it is assumed that the log spectrum of speech signals is Gaussian-distributed, which is not always valid especially when the number of mixtures in the HMMs is few. In this paper, a simple approach is proposed to improve the PMC method by splitting the mixtures before the domain transformation process in the PMC is performed, and merging the mixtures back to the original number after the PMC processes are completed. Preliminary experimental results show that the increased number of mixtures during the PMC processes can in fact provide significant improvements over the original PMC method in terms of the recognition accuracies, especially when the SNR is low.