NDLI: Speech recognition with deep recurrent neural networks

Content Provider	IEEE Xplore Digital Library
Author	Graves, A. Mohamed, A.-R. Hinton, G.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Toronto, Toronto, ON, Canada (Graves, A.; Mohamed, A.-R.; Hinton, G.)
Abstract	Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.
Starting Page	6645
Ending Page	6649
File Size	430089
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6638947
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Recurrent neural networks Training Vectors Acoustics Noise speech recognition recurrent neural networks deep neural networks
Content Type	Text
Resource Type	Article

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