The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
An investigation of deep neural networks for noise robust speech recognition
Abstract
Recently, a new acoustic model based on deep neural networks (DNN) has been introduced. While the DNN has generated significant improvements over GMM-based systems on several tasks, there has been no evaluation of the robustness of such systems to environmental distortion. In this paper, we investigate the noise robustness of DNN-based acoustic models and find that they can match state-of-the-art performance on the Aurora 4 task without any explicit noise compensation. This performance can be further improved by incorporating information about the environment into DNN training using a new method called noise-aware training. When combined with the recently proposed dropout training technique, a 7.5% relative improvement over the previously best published result on this task is achieved using only a single decoding pass and no additional decoding complexity compared to a standard DNN.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Improving 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
Speech enhancement using a minimum mean-square error log-spectral amplitude estimator
Ephraim, Y., & Malah, D. (1985) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1985.1164550 · Cited by 3,723
A Practical Guide to Training Restricted Boltzmann Machines
Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852
Acoustic Modeling Using Deep Belief Networks
Mohamed, A.-R., Dahl, G. E., & Hinton, G. (2012) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2011.2109382 · Cited by 1,727
Conversational speech transcription using context-dependent deep neural networks
Seide, F., Li, G., & Yu, D. (2011) · Interspeech 2011 · doi:10.21437/interspeech.2011-169 · Cited by 852
Self-citationRecurrent neural networks for noise reduction in robust ASR
Maas, A. L., Le, Q. V., O'Neil, T. M., Vinyals, O., Nguyen, P., & Ng, A. Y. (2012) · Interspeech 2012 · doi:10.21437/interspeech.2012-6 · Cited by 334
Mean Field Theory for Sigmoid Belief Networks
Saul, L. K., Jaakkola, T., & Jordan, M. I. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.251 · Cited by 327
HMM adaptation using vector taylor series for noisy speech recognition
Acero, A., Deng, L., Kristjansson, T., & Zhang, J. (2000) · 6th International Conference on Spoken Language Processing (ICSLP 2000) · doi:10.21437/icslp.2000-672 · Cited by 278
Connectionist probability estimators in HMM speech recognition
Renals, S., Morgan, N., Bourlard, H., Cohen, M., & Franco, H. (1994) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.260359 · Cited by 228
Large-vocabulary speech recognition under adverse acoustic environments
Deng, L., Acero, A., Plumpe, M., & Huang, X. (2000) · 6th International Conference on Spoken Language Processing (ICSLP 2000) · doi:10.21437/icslp.2000-657 · Cited by 222
Cited by
Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
Self-citationDeep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,759
A Regression Approach to Speech Enhancement Based on Deep Neural Networks
Xu, Y., Du, J., Dai, L.-R., & Lee, C.-H. (2015) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2014.2364452 · Cited by 1,366
Recent advances in deep learning for speech research at Microsoft
Deng, L., Li, J., Huang, J.-T., Yao, K., Yu, D., Seide, F., Seltzer, M., Zweig, G., He, X., Williams, J., et al. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639345 · Cited by 801
Self-citationYu, D., Yao, K., Su, H., Li, G., & Seide, F. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639201 · Cited by 408
Self-citation
Metadata
- Authors
- Seltzer, M. L., Yu, D., & Wang, Y.
- Venue
- 2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- Publisher
- IEEE
- Published
- 2013-05
- Pages
- 7398-7402
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2062164080
- Cited by
- 640
- References
- 20
- FWCI
- 52.04
- Type
- conference paper
- Language
- en
- Open access
- closed
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An investigation of deep neural networks for noise robust speech recognition. https://app.cochat.ai/papers/10.1109/icassp.2013.6639100
- The paper
- Seltzer, M. L., Yu, D., & Wang, Y. (2013). An investigation of deep neural networks for noise robust speech recognition. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. https://doi.org/10.1109/icassp.2013.6639100
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
