
- Read more about Language Model Adaptation for ASR of Spoken Translations Using Phrase-based Translation Models and Named Entity Models
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- Read more about CUED-RNNLM – An Open-Source Toolkit for Efficient Training and Evaluation of Recurrent Neural Network Language Models
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In recent years, recurrent neural network language models (RNNLMs) have become increasingly popular for a range of applications including speech recognition. However, the training of RNNLMs is computationally expensive, which limits the quantity of data, and size of network, that can be used. In order to fully exploit the power of RNNLMs, efficient training implementations are required. This paper introduces an open-source toolkit, the CUED-RNNLM toolkit, which supports efficient GPU-based training of RNNLMs.
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- Read more about Semantic Word Embedding Neural Network Language Models for Automatic Speech Recognition
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Most current language recognition systems model different levels of information such as acoustic, prosodic, phonotactic, etc. independently and combine the model likelihoods in order to make a decision. However, these are single level systems that treat all languages identically and hence incapable of exploiting any similarities that may exist within groups of languages.
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- Read more about Investigation on log-linear interpolation of multi-domain neural network language model
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