Dynamically configurable acoustic models for speech recognition

Senones were introduced to share Hidden Markov model (HMM) parameters at a sub-phonetic level in [3] and decision trees were incorporated to predict unseen phonetic contexts in [4]. In this paper, we will describe two applications of the senonic decision tree in (1) dynamically downsizing a speech recognition system for small platforms and in (2) sharing the Gaussian covariances of continuous density HMMs (CHMMs). We experimented how to balance different parameters that can offer the best trade off between recognition accuracy and system size. The dynamically downsized system, without retraining, performed even better than the regular Baum-Welch [1] trained system. The shared covariance model provided as good a performance as the unshared full model and thus gave us the freedom to increase the number of Gaussian means to increase the accuracy of the model. Combining the downsizing and covariance sharing algorithms, a total of 8% error reduction was achieved over the Baum-Welch trained system with approximately the same parameter size.