Hybrids that use a neural network to estimate the output probability for a hidden Markov model (HMM) word recognizer have been competitive with traditional HMM recognizers when both use monophone context. While traditional HMM recognizers can easily utilize more context (e.g., triphones) to achieve better results, the size of the task has made it impractical to use phonetic context directly in the neural network front end of a hybrid. In this paper, we suggest a simple method to incorporate more context by modeling the phone distributions obtained from the neural network. This allows the HMM to easily handle stochastic pronunciations as well as errors from the neural network phone recognizer. The re-estimation equations are derived for the new model. Results for the Resource Management task illustrate that SOHMM increases recognition accuracy for the cases of no grammar, unigram grammar, and word pair grammar.
['Carl D. Mitchell', 'Mary P. Harper', 'Leah H. Jamieson']
Stochastic observation hidden Markov models