Hybrid modeling of PHMM and HMM for speech recognition

A hybrid acoustic model of Partly Hidden Markov Model (PHMM) and HMM is proposed. PHMM was proposed in our previous work to deal with the complicated temporal changes of acoustic features. It can realize the observation dependent behaviors in both observations and state transitions. It achieved good performance but some errors with different trend from HMM still remained. In this paper, we designed a new acoustic model on the basis of PHMM, in which the observation and state transition probabilities are defined by the geometric means of PHMM-based ones and HMM-based ones. In this framework, if a word hypothesis is given a low score by either PHMM or HMM, it almost loses possibilities to be a probable candidate. Since many errors are due to the high-scores of incorrect categories rather than the low-score of the correct category, this property contributed to reduce errors. More over, the proposed model is more stable than PHMM because the higher order statistics of PHMM, which is generally accurate but sometimes less reliable, is smoothed by the lower order statistics of HMM, which is not so accurate but robust. Experimental results showed the effectiveness of proposed model: it reduced the word errors by 25% compared with HMM.