SPEECH RECOGNITION SYSTEM EMPLOYING DISCRIMINATIVELY TRAINED MODELS

摘要

In the speech recognition system disclosed herein, each input utterance is converted to a sequence of raw vectors. For each raw vector, the system identifies that one of a preselected plurality of quantized vectors which best matches the raw vector. The raw vector information is, however, retained for subsequent utilization. Each model of a vocabulary word to be recognized is in turn represented by sequence of states, the states being selected from a preselected group of states. However, for each word module state, there is provided both a discrete probability distribution function (pdf) and a continuous pdf characterized by preselected adjustable parameters. A stored table is provided which contains distance metric values for each combination of a quantized input vector with model state as characterized by the discrete pdfs. Word models are aligned with an input utterance using the respective discrete pdfs and initial match scores are generated using the stored table. From well matching word models identified from the initial match scores, a ranked scoring of those models is generated using the respective continuous pdfs and the raw vector information. After each utterance, the preselected continuous pdfs parameters are adjusted to increase, by a small proportion, the difference in scoring between the top and next ranking models. Preferably, if a user corrects a prior recognition event by selecting a different word model from the respective selected group, a re-adjustment of the continuous pdfs parameters is accomplished by performing, on the current state of the parameters, an adjustment opposite to that performed with the original recognition event and performing on the then current state of the parameters an adjustment equal to that which would have been performed if the newly identified different word model had been the best scoring.