Improving vocabulary independent HMM decoding results by using the dynamically expanding context

A method is presented to correct phoneme strings produced by a vocabulary independent speech recognizer. The method first extracts the N best matching result strings using mixture density hidden Markov models (HMMs) trained by neural networks. Then the strings are corrected by the rules generated automatically by the dynamically expanding context (DEC). Finally, the corrected string candidates and the extra alternatives proposed by the DEC are ranked according to the likelihood score of the best HMM path to generate the obtained string. The experiments show that N need not be very large and the method is able to decrease recognition errors from a test data that even has no common words with the training data of the speech recognizer