Phonetic Boundary Refinement using Support Vector Machine

In this paper, we propose using support vector machine (SVM) to refine the hypothesized phone transition boundaries given by the HMM-based Viterbi forced alignment. We conducted experiments on the TIMIT speech corpus. The phone transitions were automatically partitioned into 46 clusters according to their acoustic characteristics and the cross-validation using the training data; hence, 46 phone-transition-dependent SVM classifiers were used for phone boundary refinement. The proposed HMM-SVM approach performs as well as the recent discriminative HMM-based segmentation. The best accuracies achieved are 81.23% within a tolerance of 10 ms and 92.47% within a tolerance of 20 ms. The mean boundary distance is 7.73 ms.