Feature-rich continuous language models for speech recognition

@article{Mirowski2010FeaturerichCL,
  title={Feature-rich continuous language models for speech recognition},
  author={Piotr Wojciech Mirowski and Sumit Chopra and Suhrid Balakrishnan and Srinivas Bangalore},
  journal={2010 IEEE Spoken Language Technology Workshop},
  year={2010},
  pages={241-246}
}
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… 

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