PRUNING IN LIKELIHOOD EVALUATION OF HMM-BASED SPEECH RECOGNITION
In this work, we present a novel technique to reduce the likelihood computation in ASR systems that use continuous density HMMs. The proposed method, under certain conditions, only evaluates the component likelihoods of certain features, and approximates those of the remaining features by prediction. We investigate two feature clustering approaches associated with the pruning technique. While the simple sequential clustering works remarkably well, a datadriven approach performs even better in its attempt to save computation while maintaining baseline performance. With the second approach, we can achieve a saving of 45% in the likelihood evaluation for isolated word recognition, and 50% for continuous speech recognition using either monophone or triphone models. The technique is easily incorporated into any recognizer and costs only a negligible additional overhead.