The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Highway long short-term memory RNNS for distant speech recognition
Open accessAbstract
In this paper, we extend the deep long short-term memory (DL-STM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers. These direct links, called highway connections, enable unimpeded information flow across different layers and thus alleviate the gradient vanishing problem when building deeper LSTMs. We further introduce the latency-controlled bidirectional LSTMs (BLSTMs) which can exploit the whole history while keeping the latency under control. Efficient algorithms are proposed to train these novel networks using both frame and sequence discriminative criteria. Experiments on the AMI distant speech recognition (DSR) task indicate that we can train deeper LSTMs and achieve better improvement from sequence training with highway LSTMs (HLSTMs). Our novel model obtains 43.9/47.7% WER on AMI (SDM) dev and eval sets, outperforming all previous works. It beats the strong DNN and DLSTM baselines with 15.7% and 5.3% relative improvement respectively.
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260
Self-citationImproving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011
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Neural Motifs: Scene Graph Parsing with Global Context
Zellers, R., Yatskar, M., Thomson, S., & Choi, Y. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00611 · Cited by 1,052
Training Deep Nets with Sublinear Memory Cost
Chen, T., Xu, B., Zhang, C., & Guestrin, C. (2016) · arXiv · doi:10.48550/arxiv.1604.06174 · Cited by 561
Speech Emotion Recognition From 3D Log-Mel Spectrograms With Deep Learning Network
Meng, H., Yan, T., Yuan, F., & Wei, H. (2019) · IEEE Access · doi:10.1109/access.2019.2938007 · Cited by 356
An Image Captioning Model Based on Bidirectional Depth Residuals and its Application
Zhou, Z., Xu, L., Wang, C., Xie, W., Wang, S., Ge, S., & Zhang, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3057091 · Cited by 6
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Metadata
- Authors
- Zhang, Y., Chen, G., Yu, D., Yao, K., Khudanpur, S., & Glass, J.
- Venue
- 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2016-03
- Pages
- 5755-5759
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2964084166
- Cited by
- 290
- References
- 4
- FWCI
- 29.04
- Type
- conference paper
- Language
- en
- Open access
- green
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