Method and system for training language models to reduce recognition errors

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United States of America Patent

PATENT NO 10176799
APP PUB NO 20170221474A1
SERIAL NO

15013239

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Abstract

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A method and for training a language model to reduce recognition errors, wherein the language model is a recurrent neural network language model (RNNLM) by first acquiring training samples. An automatic speech recognition system (ASR) is applied to the training samples to produce recognized words and probabilites of the recognized words, and an N-best list is selected from the recognized words based on the probabilities. determining word errors using reference data for hypotheses in the N-best list. The hypotheses are rescored using the RNNLM. Then, we determine gradients for the hypotheses using the word errors and gradients for words in the hypotheses. Lastly, parameters of the RNNLM are updated using a sum of the gradients.

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Patent Owner(s)

  • MITSUBISHI ELECTRIC RESEARCH LABORATORIES, INC.

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Hershey, John Winchester, US 34 751
Hori, Chiori Lexington, US 18 158
Hori, Takaaki Lexington, US 23 264
Watanabe, Shinji Arlington, US 268 3157

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