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Multiple mixture segmental HMM and its applications

A multiple mixture segmental hidden Markov model (MMSHMM) is presented. This model is extended from the linear probabilistic-trajectory segmental HMM. Each segment is characterized by a linear trajectory with slope and mid-point parameters, and also the residual error covariances around the trajectory, so that both extra-segmental and intra-segmental variation are represented. Instead of modeling single distribution for each model parameter as earlier work, we use multiple mixture components for model parameters to represent the variability due to the variation within each speaker and also the differences between speakers. This model is evaluated on two applications. One is a phonetic classification task with TIMIT corpus, which shows that MMSHMM has advantages over conventional HMM. Another one is a speaker-independent keyword spotting task with the Road Rally database. By rescoring putative events hypothesized by a primary HMM keyword spotter, the experiments show that the performance is improved through distinguishing true hits from false alarms.