2013 SpeechRecognitionwithDeepRecurr

• (Graves et al., 2013) ⇒ Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. (2013). “Speech Recognition with Deep Recurrent Neural Networks.” In: Proceedings of 2013 International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013). ISBN:978-1-4799-0356-6 doi:10.1109/ICASSP.2013.6638947

Subject Headings: Deep Recurrent Neural Network; Recurrent Neural Network.

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Speech+Recognition+with+Deep+Recurrent+Neural+Networks

Quotes

Author Keywords

• Recurrent neural networks; deep neural networks; speech recognition

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 SpeechRecognitionwithDeepRecurr	Geoffrey E. Hinton Alex Graves Abdel-rahman Mohamed			Speech Recognition with Deep Recurrent Neural Networks				10.1109/ICASSP.2013.6638947		2013