Reducing the Computational Complexity of Two-Dimensional LSTMs
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Previous research in multi-dimensional LSTM (MD-LSTM), especially in ASR, focused on adapting the LSTM to a multi-dimensional data format.
“It should be noted that previous research in multi-dimensional LSTM (MD-LSTM) [11, 12, 13], especially in ASR [14, 15], focused on adapting the LSTM to a multi-dimensional data format.”
From Speech Emotion Recognition with Dual-Sequence LSTM Architecture (2020) · cites this paper as [15]
The proposed DS-LSTM has a distinct architecture from previous MD-LSTM and is designed to process two sequences of one-dimensional data instead of multi-dimensional data.
“Although similar in concept, our proposed DS-LSTM has a distinct architecture, and is designed to process two sequences of one-dimensional data instead of multi-dimensional data.”
From Speech Emotion Recognition with Dual-Sequence LSTM Architecture (2020) · cites this paper as [15]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Large Scale Distributed Deep Networks
Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,189 · see citing works
Long short-term memory recurrent neural network architectures for large scale acoustic modeling
Sak, H., Senior, A., & Beaufays, F. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-80 · Cited by 2,973 · see citing works
Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks
Sainath, T. N., Vinyals, O., Senior, A., & Sak, H. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178838 · Cited by 1,678 · see citing works
Self-citationHow do humans process and recognize speech?
Allen, J. B. (1994) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.326615 · Cited by 382 · see citing works
Multi-dimensional Recurrent Neural Networks
Graves, A., Fernández, S., & Schmidhuber, J. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74690-4_56 · Cited by 303 · see citing works
ReNet: A Recurrent Neural Network Based Alternative to Convolutional NetworksPreprint
Visin, F., Kastner, K., Cho, K., Matteucci, M., Courville, A. C., & Bengio, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.00393 · Cited by 258 · see citing works
Lower Frame Rate Neural Network Acoustic Models
Pundak, G., & Sainath, T. N. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-275 · Cited by 132 · see citing works
Self-citationExploring multidimensional lstms for large vocabulary ASR
Li, J., Mohamed, A., Zweig, G., & Gong, Y. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472617 · Cited by 55 · see citing works
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Speech Emotion Recognition with Dual-Sequence LSTM Architecture
Wang, J., Xue, M., Culhane, R., Diao, E., Ding, J., & Tarokh, V. (2020) · ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp40776.2020.9054629 · Cited by 162 · see citing works
- Authors
- Li, B., & Sainath, T. N.
- ORCID
- Sainath, Tara N.
- Venue
- Interspeech 2017
- Publisher
- ISCA
- Published
- 2017-08-20
- Pages
- 964-968
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W2747006787
- Cited by
- 13
- References
- 9
- Citations by year
- 2024: 2 · 2023: 2 · 2020: 1 · 2019: 3 · 2018: 4 · 2017: 1
- FWCI
- 0.72
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Interspeech (USB) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- long short-term memory networks, computational complexity reduction, 2D convolutional neural network
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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