abstract

GoldenRetriever: A Speech Recognition System Powered by Modern Information Retrieval

Authors:

Qian Xu,

Raymond Chi-Wing Wong,

Qiang YangAuthors Info & Claims

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

Pages 4500 - 4502

https://doi.org/10.1145/3394171.3414392

Published: 12 October 2020 Publication History

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Abstract

Existing Automatic Speech Recognition (ASR) systems usually generate the N-best hypotheses list first, and then rescore them with the language model score and the acoustic model score to find the best one. This procedure is essentially analogous to the working mechanism of modern Information Retrieval (IR) systems, which retrieve a relatively large amount of relevant candidates first, re-rank them, and output the top-N list. Exploiting their commonality, this demonstration proposes a novel system named GoldenRetriever that marries IR with ASR. GoldenRetriever transforms the problem of N-best hypotheses rescoring as a Learning-to-Rescore (L2RS) problem and utilizes a wide range of features beyond the language model score and the acoustic model score. In this demonstration, the audience can experience the great potential of marrying IR with ASR for the first time. GoldenRetriever should inspire more research on transferring the state-of-the-art IR techniques to ASR.

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Automatic Speech Recognition (ASR) systems usually generate the N-best hypotheses list first, and then rescore these hypotheses with the language model score and the acoustic model score. This procedure is essentially an analogy of the working mechanism of modern Information Retrieval (IR) systems, which retrieve a relatively large amount of relevant candidates first and re-rank them to output the top-N list. Exploiting their commonality, this demonstration proposes a novel system named GoldenRetriever that marries IR with ASR. GoldenRetriever transforms the problem of N-best hypotheses rescoring as a Learning-to-Rescore (L2RS) problem and utilizes a wide range of features beyond the language model score and the acoustic model score. In this demonstration, the audience can experience the great potential of marrying IR with ASR for the first time. GoldenRetriever should inspire more research on transferring the state-of-the-art IR techniques to ASR.

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References

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Cited By

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Song YWong RZhao X(2024)Speech-to-SQL: toward speech-driven SQL query generation from natural language questionThe VLDB Journal10.1007/s00778-024-00837-033:4(1179-1201)Online publication date: 16-Feb-2024
https://doi.org/10.1007/s00778-024-00837-0
Podila RKommula GK RVekkot SGupta D(2022)Telugu Dialect Speech Dataset Creation and Recognition using Deep Learning Techniques2022 IEEE 19th India Council International Conference (INDICON)10.1109/INDICON56171.2022.10040194(1-6)Online publication date: 24-Nov-2022
https://doi.org/10.1109/INDICON56171.2022.10040194
Song YJiang DZhao XHuang XXu QWong RYang QShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)SmartMeetingProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3478556(2777-2779)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3478556
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Index Terms

GoldenRetriever: A Speech Recognition System Powered by Modern Information Retrieval
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Speech recognition
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank

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Information & Contributors

Information

Published In

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

October 2020

4889 pages

ISBN:9781450379885

DOI:10.1145/3394171

General Chairs:
Chang Wen Chen
Chinese University of Hong Kong, Shenzhen, China
,
Rita Cucchiara
UNIMORE, Italy
,
Xian-Sheng Hua
Alibaba Group, China
,
Program Chairs:
Guo-Jun Qi
Futurewei Technologies, USA
,
Elisa Ricci
UNITN & Fondazione Bruno Kessler, Italy
,
Zhengyou Zhang
Tencent, China
,
Roger Zimmermann
National University of Singapore, Singapore

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2020

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HKRGC

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MM '20

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SIGMM

MM '20: The 28th ACM International Conference on Multimedia

October 12 - 16, 2020

WA, Seattle, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

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Song YWong RZhao X(2024)Speech-to-SQL: toward speech-driven SQL query generation from natural language questionThe VLDB Journal10.1007/s00778-024-00837-033:4(1179-1201)Online publication date: 16-Feb-2024
https://doi.org/10.1007/s00778-024-00837-0
Podila RKommula GK RVekkot SGupta D(2022)Telugu Dialect Speech Dataset Creation and Recognition using Deep Learning Techniques2022 IEEE 19th India Council International Conference (INDICON)10.1109/INDICON56171.2022.10040194(1-6)Online publication date: 24-Nov-2022
https://doi.org/10.1109/INDICON56171.2022.10040194
Song YJiang DZhao XHuang XXu QWong RYang QShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)SmartMeetingProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3478556(2777-2779)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3478556
Song YZhao XJiang DHuang XZhao WXu QWong RYang QShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)SmartSalesProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3478555(2774-2776)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3478555
Song YHuang XZhao XJiang DWong R(2021)Multimodal N-best List Rescoring with Weakly Supervised Pre-training in Hybrid Speech Recognition2021 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM51629.2021.00167(1336-1341)Online publication date: Dec-2021
https://doi.org/10.1109/ICDM51629.2021.00167

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