Online adaptive learning for speech recognition decoding

We describe a new method for pruning in dynamic models based on running an adaptive filtering algorithm online during decoding to predict aspects of the scores in the near future. These predictions are used to make well-informed pruning decisions during model expansion. We apply this idea to the case of dynamic graphical models and test it on a speech recognition database derived from Switchboard. Results show that significant (factor of 2) speedups can be obtained without any increase in word error rate.

• Type: Journal
• Year: 2010
• Where: INTERSPEECH
• Authors: Jeff Bilmes, Hui Lin
• Keywords: Adaptive Filtering Algorithm, Dynamic Graphical Models, INTERSPEECH 2010, Signal Processing, Well-informed Pruning Decisions