The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Hybrid speech recognition with Deep Bidirectional LSTM
Abstract
Deep Bidirectional LSTM (DBLSTM) recurrent neural networks have recently been shown to give state-of-the-art performance on the TIMIT speech database. However, the results in that work relied on recurrent-neural-network-specific objective functions, which are difficult to integrate with existing large vocabulary speech recognition systems. This paper investigates the use of DBLSTM as an acoustic model in a standard neural network-HMM hybrid system. We find that a DBLSTM-HMM hybrid gives equally good results on TIMIT as the previous work. It also outperforms both GMM and deep network benchmarks on a subset of the Wall Street Journal corpus. However the improvement in word error rate over the deep network is modest, despite a great increase in framelevel accuracy. We conclude that the hybrid approach with DBLSTM appears to be well suited for tasks where acoustic modelling predominates. Further investigation needs to be conducted to understand how to better leverage the improvements in frame-level accuracy towards better word error rates.
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Self-citationBidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
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Self-citationKeeping the neural networks simple by minimizing the description length of the weights
Hinton, G. E., & van Camp, D. (1993) · Proceedings of the sixth annual conference on Computational learning theory - COLT '93 · doi:10.1145/168304.168306 · Cited by 994
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Metadata
- Authors
- Graves, A., Jaitly, N., & Mohamed, A.-R.
- Venue
- 2013 IEEE Workshop on Automatic Speech Recognition and Understanding
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 273-278
- OpenAlex
- W2005708641
- Cited by
- 1,736
- References
- 5
- FWCI
- 32.96
- Type
- conference paper
- Language
- en
- Open access
- closed
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