AWARD  |  Best Paper Award at the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) 2019

Date released: December 18, 2019


  •  AWARD   Best Paper Award at the IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) 2019
  • Date:

    December 18, 2019

  • Awarded to:

    Xuankai Chang, Wangyou Zhang, Yanmin Qian, Jonathan Le Roux, Shinji Watanabe

  • Description:

    MERL researcher Jonathan Le Roux and co-authors Xuankai Chang, Shinji Watanabe (Johns Hopkins University), Wangyou Zhang, and Yanmin Qian (Shanghai Jiao Tong University) won the Best Paper Award at the 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU 2019), for the paper "MIMO-Speech: End-to-End Multi-Channel Multi-Speaker Speech Recognition". MIMO-Speech is a fully neural end-to-end framework that can transcribe the text of multiple speakers speaking simultaneously from multi-channel input. The system is comprised of a monaural masking network, a multi-source neural beamformer, and a multi-output speech recognition model, which are jointly optimized only via an automatic speech recognition (ASR) criterion. The award was received by lead author Xuankai Chang during the conference, which was held in Sentosa, Singapore from December 14-18, 2019.

  • MERL Contact:
  • External Link:

    http://asru2019.org/

  • Research Areas:

    Artificial Intelligence, Machine Learning, Speech & Audio

    •  Chang, X., Zhang, W., Qian, Y., Le Roux, J., Watanabe, S., "MIMO-Speech: End-to-End Multi-Channel Multi-Speaker Speech Recognition", IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), December 2019, pp. 237-144.
      BibTeX TR2019-157 PDF
      • @inproceedings{Chang2019dec,
      • author = {Chang, Xuankai and Zhang, Wangyou and Qian, Yanmin and Le Roux, Jonathan and Watanabe, Shinji},
      • title = {MIMO-Speech: End-to-End Multi-Channel Multi-Speaker Speech Recognition},
      • booktitle = {IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU)},
      • year = 2019,
      • pages = {237--144},
      • month = dec,
      • isbn = {978-1-7281-0305-1},
      • url = {https://www.merl.com/publications/TR2019-157}
      • }