Research on language modeling for speech recognition has increasingly focused on the application of neural networks. Two competing concepts have been developed: On the one hand, feedforward neural networks representing an ngram approach, on the other hand recurrent neural networks that may learn context dependencies spanning more than a fixed number of predecessor words.To the best of our knowledge, no comparison has been carried out between feedforward and state-of-the-art recurrent networks when applied to speech recognition. This paper analyzes this aspect in detail on a well-tuned French speech recognition task. In addition, we propose a simple and efficient method to normalize language model probabilities across different vocabularies, and we show how to speed up training of recurrent neural networks by parallelization.

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This paper describes recent advances at LIMSI in Mandarin Chinese speech-to-text transcription. A number of novel approaches were introduced in the different system components. The acoustic models are trained on over 1600 hours of audio data from a range of sources, and include pitch and MLP features. N-gram and neural network language models are trained on very large corpora, over 3 billion words of texts; and LM adaptation was explored at different adaptation levels: per show, per snippet, or per speaker cluster. Character-based consensus decoding was found to outperform word-based consensus decoding for Mandarin. The improved system reduces the relative character error rate (CER) by about 10% on previous GALE development and evaluation data sets, obtaining a CER of 9.2% on the P4 broadcast news and broadcast conversation evaluation data.