Extended partial distance elimination and dynamic Gaussian selection for fast likelihood computation

A new fast likelihood computation approach is proposed for HMM-based continuous speech recognition. This approach is an extension of the partial distance elimination (PDE) technique. Like PDE, the extended PDE (EPDE) approach aims at finding the most prominent Gaussian in a GMM for a given observation, and approximating the GMM’s likelihood with the identified Gaussian. EPDE relies on a novel selection criterion in order to achieve greater time efficiency at the cost of slight degradation of recognition accuracy. This novel criterion has been combined with a dynamic Gaussian selection technique for greater recognition accuracy. Tests on TIMIT corpora shows a satisfying computation time saving of 7.3% at the same error level as PDE. Compared to a baseline, the methods we propose have also achieved a significant reduction in the number of computations of 71.5% at the same error level as PDE.