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Feature-rich continuous language models for speech recognition

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4 Author(s)
Mirowski, P. ; Courant Inst. of Math. Sci., New York Univ., New York, NY, USA ; Chopra, S. ; Balakrishnan, S. ; Bangalore, S.

State-of-the-art probabilistic models of text such as n-grams require an exponential number of examples as the size of the context grows, a problem that is due to the discrete word representation. We propose to solve this problem by learning a continuous-valued and low-dimensional mapping of words, and base our predictions for the probabilities of the target word on non-linear dynamics of the latent space representation of the words in context window. We build on neural networks-based language models; by expressing them as energy-based models, we can further enrich the models with additional inputs such as part-of-speech tags, topic information and graphs of word similarity. We demonstrate a significantly lower perplexity on different text corpora, as well as improved word accuracy rate on speech recognition tasks, as compared to Kneser-Ney back-off n-gram-based language models.

Published in:

Spoken Language Technology Workshop (SLT), 2010 IEEE

Date of Conference:

12-15 Dec. 2010