Fast Decoding Algorithm for Automatic Speech Recognition Based on Recurrent Neural Networks

ZHANG Ge; ZHANG Pengyuan; PAN Jielin; YAN Yonghong

doi:10.11999/JEIT160543

Volume 39 Issue 4

Apr. 2017

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ZHANG Ge, ZHANG Pengyuan, PAN Jielin, YAN Yonghong. Fast Decoding Algorithm for Automatic Speech Recognition Based on Recurrent Neural Networks[J]. Journal of Electronics & Information Technology, 2017, 39(4): 930-937. doi: 10.11999/JEIT160543

Citation:

ZHANG Ge, ZHANG Pengyuan, PAN Jielin, YAN Yonghong. Fast Decoding Algorithm for Automatic Speech Recognition Based on Recurrent Neural Networks[J]. Journal of Electronics & Information Technology, 2017, 39(4): 930-937. doi: 10.11999/JEIT160543

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Fast Decoding Algorithm for Automatic Speech Recognition Based on Recurrent Neural Networks

doi: 10.11999/JEIT160543

Funds:

The National Natural Science Foundation of China (U1536117, 11590770-4), The National Key Research and Development Plan of China (2016YFB0801200, 2016YFB0801203), The Key Science and Technology Project of the Xinjiang Uygur Autonomous Region (2016A03007-1)

Received Date: 2016-05-26
Rev Recd Date: 2017-01-09
Publish Date: 2017-04-19

Abstract

Abstract

Recurrent Neural Networks (RNN) are widely used for acoustic modeling in Automatic Speech Recognition (ASR). Although RNNs show many advantages over traditional acoustic modeling methods, the inherent higher computational cost limits its usage, especially in real-time applications. Noticing that the features used by RNNs usually have relatively long acoustic contexts, it is possible to lower the computational complexity of both posterior calculation and token passing process with overlapped information. This paper introduces a novel decoder structure that drops the overlapped acoustic frames regularly, which leads to a significant computational cost reduction in the decoding process. Especially, the new approach can directly use the original RNNs with minor modifications on the HMM topology, which makes it flexible. In experiments on conversation telephone speech datasets, this approach achieves 2 to 4 times speedup with little relative accuracy reduction.
- Speech recognition,
- Recurrent Neural Network (RNN),
- Decoder,
- Frame skipping

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References(18)

References

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