METHOD AND SYSTEM TO SCALE DOWN A DECISION TREE-BASED HIDDEN MARKOV MODEL (HMM) FOR SPEECH RECOGNITION

摘要

A method and system are provided in which a decision tree-based model ('general model') is scaled down ('trim-down') for a given task. The trim-down model can be adapted for the given task using task specific data. The general model can be based on a hidden markov model (HMM). By allowing a decision tree-based acoustic model ('general model') to be scaled according to the vocabulary of the given task, the general model can be configured dynamically into a trim-down model, which can be used to improve speech recognition performance and reduce system resource utilization. Furthermore, the trim-down model can be adapted/adjusted according to task specific data, e.g., task vocabulary, model size, or other like task specific data.