Mixture Pruning and Roughening for Scalable Acoustic Models

In an automatic speech recognition system using a tied-mixture acoustic model, the main cost in CPU time and memory lies not in the evaluation and storage of Gaussians themselves but rather in evaluating the mixture likelihoods for each state output distribution. Using a simple entropy-based technique for pruning the mixture weight distributions, we can achieve a significant speedup in recognition for a 5000-word vocabulary with a negligible increase in word error rate. This allows us to achieve real-time connected-word dictation on an ARM-based mobile device.