Date of Original Version
2008
Type
Conference Proceeding
Published In
Proceedings of ACL-08 Workshop on Mobile Language Processing, Columbus, OH, USA, June 2008
Abstract or Table of Contents
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.
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