Transition probabilities are more important than we once thought

It is generally believed that the transition probabilities in a hidden Markov model (HMM) have a limited role in the speech decoding process. In this paper, through a series of recognition experiments on Wall Street Journal (WSJ) read speech and SVitchboard (SVB) conversational telephone speech, we find that the HMM transition probabilities may be more important than we once thought. The experiments include: (1) setting or not setting all outgoing transition probabilities equal; (2) the introduction of word-final triphones and the re-estimation of their transition probabilities; (3) besides grammar factor and insertion penalty, the addition of a third decoding parameter called transition factor to scale the transition probability score during decoding. The results of the above three experiments enable us to improve the the word accuracy of the WSJ and SVB speech recognition task by 0.7% and 5.3% absolute respectively when compared to their baseline model in which all transition probabilities are simply set to 0.5.