The DOI matches its Crossref record; 58 of the 60 references it cites were found in a registry or an index.
The Microsoft 2017 Conversational Speech Recognition System
Open accessAbstract
We describe the latest version of Microsoft's conversational speech recognition system for the Switchboard and CallHome domains. The system adds a CNN-BLSTM acoustic model to the set of model architectures we combined previously, and includes character-based and dialog session aware LSTM language models in rescoring. For system combination we adopt a two-stage approach, whereby acoustic model posteriors are first combined at the senone/frame level, followed by a word-level voting via confusion networks. We also added another language model rescoring step following the confusion network combination. The resulting system yields a 5.1% word error rate on the NIST 2000 Switchboard test set, and 9.8% on the CallHome subset.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Importantly, a 24% word error rate is probably not yet sufficiently low for everyday use (for example, compared with a 4–5% word error rate for state-of-the-art speech-to-text systems23,37).”
From A high-performance speech neuroprosthesis (2023) · cites this paper as [23]
In the citing paper
“They are, however, not limited to image processing and analysis but are outperforming other approaches in areas like natural language processing [3, 4, 5], speech recognition and synthesis [6, 7] 2 , and in the analysis of unstructured, tabular-type data using entity embeddings [8, 9].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [6]
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
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Framewise phoneme classification with bidirectional LSTM and other neural network architectures
Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 6,152
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390
Convolutional networks for images, speech, and time series
LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,646
SRILM - an extensible language modeling toolkit
Stolcke, A. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-303 · Cited by 4,385
Self-citationA Learning Algorithm for Continually Running Fully Recurrent Neural Networks
Williams, R. J., & Zipser, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.2.270 · Cited by 4,369
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
Front-End Factor Analysis for Speaker Verification
Dehak, N., Kenny, P. J., Dehak, R., Dumouchel, P., & Ouellet, P. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2010.2064307 · Cited by 3,549
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
Linguistic Regularities in Continuous Space Word Representations
Mikolov, T., Yih, W.-T., & Zweig, G. (2013) · Cited by 2,913
Phoneme recognition using time-delay neural networks
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., & Lang, K. J. (1989) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/29.21701 · Cited by 2,643
SWITCHBOARD: telephone speech corpus for research and development
Godfrey, J. J., Holliman, E. C., & McDaniel, J. (1992) · [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1992.225858 · Cited by 2,134 · cited 1×
“These systems typically use deep convolutional neural network (CNN) architectures in acoustic modeling, and multi-layered recurrent networks with gated memory (long-short-term memory, LSTM [8]) models for both acoustic and language modeling, driving the word error rate on the benchmark Switchboard corpus [20] down fro…”
BackgroundLSTM neural networks for language modeling
Sundermeyer, M., Schlüter, R., & Ney, H. (2012) · Interspeech 2012 · doi:10.21437/interspeech.2012-65 · Cited by 1,963
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
Conceptual pacts and lexical choice in conversation.
Brennan, S. E., & Clark, H. H. (1996) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037//0278-7393.22.6.1482 · Cited by 1,529
Generalization of back-propagation to recurrent neural networks
Pineda, F. J. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.2229 · Cited by 931
Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015) · arXiv · doi:10.48550/arxiv.1505.00387 · Cited by 912
Conversational speech transcription using context-dependent deep neural networks
Seide, F. T. B., Li, G., & Yu, D. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-169 · Cited by 877
Purely Sequence-Trained Neural Networks for ASR Based on Lattice-Free MMI
Povey, D., Peddinti, V., Galvez, D., Ghahremani, P., Manohar, V., Na, X., Wang, Y., & Khudanpur, S. P. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-595 · Cited by 746
Using the Output Embedding to Improve Language Models
Press, O., & Wolf, L. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-2025 · Cited by 729
Achieving Human Parity in Conversational Speech RecognitionPreprint
Xiong, W., Droppo, J., Huang, X.-D., Seide, F., Seltzer, M., Stolcke, A., Yu, D., & Zweig, G. (2016) · arXiv.org · doi:10.48550/arxiv.1610.05256 · Cited by 600
Self-citationContext dependent recurrent neural network language model
Mikolov, T., & Zweig, G. (2012) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2012.6424228 · Cited by 596
Speaker adaptation of neural network acoustic models using i-vectors
Saon, G., Soltau, H., Nahamoo, D., & Picheny, M. (2013) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2013.6707705 · Cited by 586
Neural Probabilistic Language Models
Bengio, Y., Schwenk, H., Senécal, J.-S., Morin, F., & Gauvain, J. (2006) · Springer eBooks · doi:10.1007/3-540-33486-6_6 · Cited by 557
Sequencing In Conversational Openings
Schegloff, E. A. (1972) · De Gruyter eBooks · doi:10.1515/9783110880434-006 · Cited by 517
From Feedforward to Recurrent LSTM Neural Networks for Language Modeling
Sundermeyer, M., Ney, H., & Schlüter, R. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2400218 · Cited by 515
Seide, F., Fu, H., Droppo, J., Li, G., & Yu, D. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-274 · Cited by 443
Self-citationFast and accurate recurrent neural network acoustic models for speech recognition
Sak, H., Senior, A., Rao, K., & Beaufays, F. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-350 · Cited by 426
English Conversational Telephone Speech Recognition by Humans and Machines
Saon, G., Kurata, G., Sercu, T., Audhkhasi, K., Thomas, S., Dimitriadis, D., Cui, X., Ramabhadran, B., Picheny, M., Lim, L.-L., et al. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-405 · Cited by 364
Very Deep Convolutional Neural Networks for Noise Robust Speech Recognition
Qian, Y., Bi, M., Tan, T., & Yu, K. (2016) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2016.2602884 · Cited by 346
An Introduction to Computational Networks and the Computational Network Toolkit
Yu, D., Eversole, A., Seltzer, M., Yao, K., Kuchaiev, O., Zhang, Y., Seide, F. T. B., Huang, Z., Guenter, B. K., Wang, H., et al. (2014) · Cited by 332
Self-citationThe microsoft 2016 conversational speech recognition system
Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M., Stolcke, A., Yu, D., & Zweig, G. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7953159 · Cited by 326
Self-citationPhoneme Recognition Using Time-Delay Neural Networks
Waibel, A. H., Hanazawa, T., Hinton, G. E., Shikano, K., & LANG, K. J. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051584-7.50037-1 · Cited by 313
Statistical language model adaptation: review and perspectives
Bellegarda, J. R. (2003) · Speech Communication · doi:10.1016/j.specom.2003.08.002 · Cited by 305
Shriberg, E. E., Stolcke, A., & Baron, D. (2001) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.2001-352 · Cited by 224
Self-citationVery deep multilingual convolutional neural networks for LVCSR
Sercu, T., Puhrsch, C., Kingsbury, B. E. D., & LeCun, Y. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472620 · Cited by 211
THE SRI MARCH 2000 HUB-5 CONVERSATIONAL SPEECH TRANSCRIPTION SYSTEM
Stolcke, A., Bratt, H., Butzberger, J., Franco, H., Gadde, V. R. R., Richey, C., Shriberg, E. E., Weng, F. Y., & Zheng, J. (2000) · Cited by 147
Self-citationA recurrent error propagation network speech recognition system
Robinson, T., & Fallside, F. (1991) · Computer Speech & Language · doi:10.1016/0885-2308(91)90010-n · Cited by 140
INSIGHTS INTO SPOKEN LANGUAGE GLEANED FROM PHONETIC TRANSCRIPTION OF THE SWITCHBOARD CORPUS
Greenberg, S., Hollenback, J., & Ellis, D. P. W. (1996) · Cited by 139
Bulyko, I., Mari Ostendorf, E., & Stolcke, A. (2003) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1073483.1073486 · Cited by 137
Self-citationComputing consensus translation from multiple machine translation systems
Bangalore, B., Bordel, G., & Riccardi, G. (2005) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2001.1034659 · Cited by 129
Advances in speech transcription at IBM under the DARPA EARS program
Chen, S. F., Kingsbury, B. E. D., Mangu, L., Povey, D., Saon, G., Soltau, H., & Zweig, G. (2006) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2006.879814 · Cited by 125
The IBM 2016 English Conversational Telephone Speech Recognition System
Saon, G., Sercu, T., Rennie, S. J., & Kuo, H.-K. J. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-1460 · Cited by 106
The IBM 2015 English conversational telephone speech recognition system
Saon, G., Kuo, H.-K. J., Rennie, S. J., & Picheny, M. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-632 · Cited by 92
Deep Convolutional Neural Networks with Layer-Wise Context Expansion and Attention
Yu, D., Xiong, W., Droppo, J., Stolcke, A., Ye, G., Li, J., & Zweig, G. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-251 · Cited by 83
Self-citationComparing Human and Machine Errors in Conversational Speech Transcription
Stolcke, A., & Droppo, J. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1544 · Cited by 68
Self-citationConsonant Recognition by Modular Construction of Large Phonemic Time-Delay Neural Networks
Waibel, A., Sawai, H., & Shikano, K. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051584-7.50038-3 · Cited by 58
Very deep convolutional neural networks for LVCSR
Bi, M., Qian, Y., & Yu, K. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-656 · Cited by 44
Multi-speaker language modeling
Ji, G., & Bilmes, J. (2004) · doi:10.3115/1613984.1614018 · Cited by 26
Deep Learning-Based Telephony Speech Recognition in the Wild
Han, K. J., Hahm, S., Kim, B., Kim, J., & Lane, I. R. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1695 · Cited by 25
Linearly augmented deep neural network
Ghahremani, P., Droppo, J., & Seltzer, M. L. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472646 · Cited by 21
Self-citationImproving English Conversational Telephone Speech Recognition
Medennikov, I., Prudnikov, A., & Zatvornitskiy, A. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-473 · Cited by 21
Transcription Methods for Consistency, Volume and Efficiency
Glenn, M. L., Strassel, S. M., Lee, H., Maeda, K., Zakhary, R., & Li, X. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3t2go3yc49ar · Cited by 16
Self-stabilized deep neural network
Ghahremani, P., & Droppo, J. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7472719 · Cited by 9
Self-citation
Research works printed but not linked: 2
- 0, The Microsoft Cognition Toolkit (CNTK)
- xiong, 2017, Conversational session-level language modeling with LSTMs, Submitted to IEEE ICASSP-2018
Cited by
Sorted by citations
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012
Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
Zhang, P., Lan, C., Zeng, W., Xing, J., Xue, J., & Zheng, N. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00119 · Cited by 692
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting
Yuan, Y., Weng, X., Ou, Y., & Kitani, K. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00967 · Cited by 715
Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction
Yu, C., Ma, X., Ren, J., Zhao, H., & Yi, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58610-2_30 · Cited by 645
A high-performance speech neuroprosthesis
Willett, F. R., Kunz, E. M., Fan, C., Avansino, D. T., Wilson, G. H., Choi, E. Y., Kamdar, F., Glasser, M. F., Hochberg, L. R., Druckmann, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06377-x · Cited by 581
Metadata
- Authors
- Xiong, W., Wu, L., Alleva, F., Droppo, J., Huang, X., & Stolcke, A.
- Affiliations
- Microsoft (United States) (US)
- Venue
- 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2018-04
- Pages
- 5934-5938
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2916979304
- Cited by
- 472
- References
- 60
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 38 · 2023: 33 · 2022: 59 · 2021: 72 · 2020: 96 · 2019: 103 · 2018: 35
- FWCI
- 30.44
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- conversational speech recognition, switchboards, CALLHOME, LSTM language models, confusion networks, system combination, word error rate
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
