Feature pruning in likelihood evaluation of HMM-based speech recognition

In this work, we present a simple yet effective technique to reduce the likelihood computation in ASR systems that use continuous density HMM. In a variety of speech recognition tasks, likelihood evaluation accounts for a significant portion of the total computational load. Our proposed method, under certain conditions, only evaluates the component likelihoods of certain features, and approximates those of the remaining (pruned) features by prediction. We investigate two feature clustering approaches associated with our pruning technique. While a simple sequential clustering works remarkably well, a data-driven approach performs even better in its attempt to save computation while maintaining baseline recognition accuracy. With the second approach, we can speed up the likelihood evaluation by 33% and reduce its power consumption by 27% for an isolated word recognition task. For a continuous speech recognition system using either monophone or triphone models, the speedup and power reduction of the likelihood evaluation are 50% and 35% respectively.