Improving speech recognition accuracy with contextual phonemes and MMI training

The authors experiment with a combination of two methods previously proposed to improve the performance of their speech recognition system. One method is based on the definition of an improved system of phonetic units, which takes into account the most important coarticulation effects. This system has been defined using knowledge about coarticulation, and by studying the errors of a standard phonetic system. The second method is based on the use of maximum mutual information (MMI) as a criterion in the training phase of the speech recognition system. MMI is designed to maximize the probability of the correct text versus the other possible texts, and it is expected to provide better discrimination of the correct text than the standard maximum-likelihood criterion. These methods have been tested independently on phonetic recognition, and each of them improved the recognition accuracy of the system. Results of recognition experiments that combine the two methods are presented and discussed. They show that this combination improves the average recognition rate, both for phonetic and word recognition