New approaches for domain transformation and parameter combination for improved accuracy in parallel model combination (PMC) techniques

Abstract

Parallel model combination (PMC) techniques have been very successful and popularly used in many applications to improve the performance of speech recognition systems under noisy environments. However, it is believed that some assumptions and approximations made in this approach, primarily in the domain transformation and parameter combination processes, are not necessarily accurate enough in certain practical situations, which may degrade the achievable performance of PMC. In this paper, the possible sources that cause the performance degradation in these processes are carefully analyzed and discussed. Three new approaches, including the truncated Gaussian approach and the split mixture approach for the domain transformation process and the estimated cross-term approach for parameter combination process, are proposed in this paper in order to handle these problems, minimize such degradation, and improve the accuracy of the PMC techniques. These proposed approaches were analyzed and discussed with two recognition tasks, one relatively simple, and the other more complicated and realistic. Both sets of experiments showed that these proposed approaches are able to provide significant improvements over the original PMC method, especially when the SNR condition is worse.