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- W177470723 abstract "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 (approximately factor of 2) speedups can be obtained without any increase in word error rate or increase in memory usage." @default.
- W177470723 created "2016-06-24" @default.
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- W177470723 date "2010-09-26" @default.
- W177470723 modified "2023-10-01" @default.
- W177470723 title "Online adaptive learning for speech recognition decoding" @default.
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- W177470723 doi "https://doi.org/10.21437/interspeech.2010-100" @default.
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