A fuzzy-clustering analysis based on phonetic tied-mixture HMM

To efficiently decrease the parameter size and improve the robustness of parameter training, a revaluing fuzzy-clustering based on Phonetic Tied-mixture HMM (PTM), i.e. FPTM, was presented. The FPTM Gaussian code book was synthesized from all Gaussians belong to the same root node in phonetic decision tree. The fuzzy-clustering method was further used for FPTM covariance sharing. Experimental results showed that compared with the conventional PTM with approximately the same parameter size, the size of FPTM weights decreased by 77.59% and recognition rate increased by 7.92%, and compared covariance-shared FPTM with tri-phone model, the former error rate was reduced by 3.01%.