NEWS  |  MERL's speech research featured in NPR's All Things Considered

Date released: Feb 9, 2018


  •  NEWS   MERL's speech research featured in NPR's All Things Considered
  • Date:

    February 5, 2018

  • Description:

    MERL's speech separation technology was featured in NPR's All Things Considered, as part of an episode of All Tech Considered on artificial intelligence, "Can Computers Learn Like Humans?". An example separating the overlapped speech of two of the show's hosts was played on the air.
    The technology is based on a proprietary deep learning method called Deep Clustering. It is the world's first technology that separates in real time the simultaneous speech of multiple unknown speakers recorded with a single microphone. It is a key step towards building machines that can interact in noisy environments, in the same way that humans can have meaningful conversations in the presence of many other conversations.
    A live demonstration was featured in Mitsubishi Electric Corporation's Annual R&D Open House last year, and was also covered in international media at the time.

    (Photo credit: Sam Rowe for NPR)

    Link:
    "Can Computers Learn Like Humans?" (NPR, All Things Considered)
    MERL Deep Clustering Demo

  • Where:

    National Public Radio (NPR)

  • MERL Contact:
  • Research Areas:

    Speech & Audio, Artificial Intelligence

  • Related Publications:
  •  Isik, Y., Le Roux, J., Chen, Z., Watanabe, S., Hershey, J.R., "Single-Channel Multi-Speaker Separation using Deep Clustering", Interspeech, DOI: 10.21437/Interspeech.2016-1176, September 2016, pp. 545-549.
  •  Hershey, J.R.; Chen, Z.; Le Roux, J.; Watanabe, S., "Deep Clustering: Discriminative Embeddings for Segmentation and Separation", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP.2016.7471631, March 2016, pp. 31-35.
    BibTeX Download PDFAbout TR2016-003
    • @inproceedings{Hershey2016mar,
    • author = {Hershey, J.R. and Chen, Z. and {Le Roux}, J. and Watanabe, S.},
    • title = {Deep Clustering: Discriminative Embeddings for Segmentation and Separation},
    • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
    • year = 2016,
    • pages = {31--35},
    • month = mar,
    • doi = {10.1109/ICASSP.2016.7471631},
    • url = {http://www.merl.com/publications/TR2016-003}
    • }