Cited by 472Speech Recognition and SynthesisSpeech and Audio ProcessingSpeech and dialogue systems

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The Microsoft 2017 Conversational Speech Recognition System

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Xiong, W., Wu, L., Alleva, F., Droppo, J., Huang, X., Stolcke, A.·2018·2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

DOI 10.1109/icassp.2018.8461870

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Abstract

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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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

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Authors: Xiong, W., Wu, L., Alleva, F., Droppo, J., Huang, X., & Stolcke, A.
Affiliations: Microsoft (United States) (US)
ORCID: Xiong, W.
Droppo, J.
Huang, X.
Stolcke, A.
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)
DOI: 10.1109/icassp.2018.8461870
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

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The Microsoft 2017 Conversational Speech Recognition System

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