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.
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A 24% word error rate is probably not yet sufficiently low for everyday use.
“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]
Deep learning techniques are outperforming other approaches in natural language processing, speech recognition and synthesis, and analysis of unstructured tabular data using entity embeddings.
“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]
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- 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 · 2017: 6 · 2013: 1
- 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.
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