Chiori Hori

Chiori Hori
  • Biography

    Chiori has been a member of MERL's research team since 2015. Her work is focused on spoken dialog and audio visual scene-aware dialog technologies toward human-robot communications. She's on the editorial board of "Computer Speech and Language" and is a technical committee member of "Speech and Language Processing Group" of IEEE Signal Processing Society. Prior to joining MERL, Chiori spent 8 years at Japan's National Institute of Information and Communication Technology (NICT), where she held the position of Research Manager of the Spoken Language Communication Laboratory. She also spent time researching at Carnegie Mellon and the NTT Communication Science Laboratories, prior to NICT.

  • Recent News & Events

    •  NEWS   Chiori Hori will give keynote on scene understanding via multimodal sensing at AI Electronics Symposium
      Date: February 15, 2021
      Where: The 2nd International Symposium on AI Electronics
      MERL Contact: Chiori Hori
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
      Brief
      • Chiori Hori, a Senior Principal Researcher in MERL's Speech and Audio Team, will be a keynote speaker at the 2nd International Symposium on AI Electronics, alongside Alex Acero, Senior Director of Apple Siri, Roberto Cipolla, Professor of Information Engineering at the University of Cambridge, and Hiroshi Amano, Professor at Nagoya University and winner of the Nobel prize in Physics for his work on blue light-emitting diodes. The symposium, organized by Tohoku University, will be held online on February 15, 2021, 10am-4pm (JST).

        Chiori's talk, titled "Human Perspective Scene Understanding via Multimodal Sensing", will present MERL's work towards the development of scene-aware interaction. One important piece of technology that is still missing for human-machine interaction is natural and context-aware interaction, where machines understand their surrounding scene from the human perspective, and they can share their understanding with humans using natural language. To bridge this communications gap, MERL has been working at the intersection of research fields such as spoken dialog, audio-visual understanding, sensor signal understanding, and robotics technologies in order to build a new AI paradigm, called scene-aware interaction, that enables machines to translate their perception and understanding of a scene and respond to it using natural language to interact more effectively with humans. In this talk, the technologies will be surveyed, and an application for future car navigation will be introduced.
    •  
    •  NEWS   MERL's Scene-Aware Interaction Technology Featured in Mitsubishi Electric Corporation Press Release
      Date: July 22, 2020
      Where: Tokyo, Japan
      MERL Contacts: Anoop Cherian; Chiori Hori; Takaaki Hori; Jonathan Le Roux; Tim Marks; Alan Sullivan; Anthony Vetro
      Research Areas: Artificial Intelligence, Computer Vision, Machine Learning, Speech & Audio
      Brief
      • Mitsubishi Electric Corporation announced that the company has developed what it believes to be the world’s first technology capable of highly natural and intuitive interaction with humans based on a scene-aware capability to translate multimodal sensing information into natural language.

        The novel technology, Scene-Aware Interaction, incorporates Mitsubishi Electric’s proprietary Maisart® compact AI technology to analyze multimodal sensing information for highly natural and intuitive interaction with humans through context-dependent generation of natural language. The technology recognizes contextual objects and events based on multimodal sensing information, such as images and video captured with cameras, audio information recorded with microphones, and localization information measured with LiDAR.

        Scene-Aware Interaction for car navigation, one target application, will provide drivers with intuitive route guidance. The technology is also expected to have applicability to human-machine interfaces for in-vehicle infotainment, interaction with service robots in building and factory automation systems, systems that monitor the health and well-being of people, surveillance systems that interpret complex scenes for humans and encourage social distancing, support for touchless operation of equipment in public areas, and much more. The technology is based on recent research by MERL's Speech & Audio and Computer Vision groups.


        Demonstration Video:



        Link:

        Mitsubishi Electric Corporation Press Release
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  • Research Highlights

  • MERL Publications

    •  Geng, S., Gao, P., Chatterjee, M., Hori, C., Le Roux, J., Zhang, Y., Li, H., Cherian, A., "Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers", AAAI Conference on Artificial Intelligence, February 2021.
      BibTeX TR2021-010 PDF
      • @inproceedings{Geng2021feb,
      • author = {Geng, Shijie and Gao, Peng and Chatterjee, Moitreya and Hori, Chiori and Le Roux, Jonathan and Zhang, Yongfeng and Li, Hongsheng and Cherian, Anoop},
      • title = {Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers},
      • booktitle = {AAAI Conference on Artificial Intelligence},
      • year = 2021,
      • month = feb,
      • url = {https://www.merl.com/publications/TR2021-010}
      • }
    •  Hori, T., Moritz, N., Hori, C., Le Roux, J., "Transformer-based Long-context End-to-end Speech Recognition", Annual Conference of the International Speech Communication Association (Interspeech), DOI: 10.21437/​Interspeech.2020-2928, October 2020, pp. 5011-5015.
      BibTeX TR2020-139 PDF
      • @inproceedings{Hori2020oct,
      • author = {Hori, Takaaki and Moritz, Niko and Hori, Chiori and Le Roux, Jonathan},
      • title = {Transformer-based Long-context End-to-end Speech Recognition},
      • booktitle = {Annual Conference of the International Speech Communication Association (Interspeech)},
      • year = 2020,
      • pages = {5011--5015},
      • month = oct,
      • doi = {10.21437/Interspeech.2020-2928},
      • issn = {1990-9772},
      • url = {https://www.merl.com/publications/TR2020-139}
      • }
    •  Gao, P., Hori, C., Geng, S., Hori, T., Le Roux, J., "Multi-Pass Transformer for Machine Translation", arXiv, September 2020.
      BibTeX arXiv
      • @article{Gao2020sep,
      • author = {Gao, Peng and Hori, Chiori and Geng, Shijie and Hori, Takaaki and Le Roux, Jonathan},
      • title = {Multi-Pass Transformer for Machine Translation},
      • journal = {arXiv},
      • year = 2020,
      • month = sep,
      • url = {http://arxiv.org/abs/2009.11382}
      • }
    •  Geng, S., Gao, P., Hori, C., Le Roux, J., Cherian, A., "Spatio-Temporal Scene Graphs for Video Dialog", arXiv, July 2020.
      BibTeX arXiv
      • @article{Geng2020jul,
      • author = {Geng, Shijie and Gao, Peng and Hori, Chiori and Le Roux, Jonathan and Cherian, Anoop},
      • title = {Spatio-Temporal Scene Graphs for Video Dialog},
      • journal = {arXiv},
      • year = 2020,
      • month = jul,
      • url = {https://arxiv.org/abs/2007.03848}
      • }
    •  Shi, L., Geng, S., Shuang, K., Hori, C., Liu, S., Gao, P., Su, S., "Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/​ICASSP40776.2020.9053595, April 2020, pp. 4412-4416.
      BibTeX TR2020-046 PDF
      • @inproceedings{Shi2020apr,
      • author = {Shi, Lei and Geng, Shijie and Shuang, Kai and Hori, Chiori and Liu, Songxiang and Gao, Peng and Su, Sen},
      • title = {Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering},
      • booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      • year = 2020,
      • pages = {4412--4416},
      • month = apr,
      • publisher = {IEEE},
      • doi = {10.1109/ICASSP40776.2020.9053595},
      • issn = {2379-190X},
      • isbn = {978-1-5090-6631-5},
      • url = {https://www.merl.com/publications/TR2020-046}
      • }
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  • MERL Issued Patents

    • Title: "Method and System for Multi-Modal Fusion Model"
      Inventors: Hori, Chiori; Hori, Takaaki; Hershey, John R.; Marks, Tim
      Patent No.: 10,417,498
      Issue Date: Sep 17, 2019
    • Title: "Method and System for Training Language Models to Reduce Recognition Errors"
      Inventors: Hori, Takaaki; Hori, Chiori; Watanabe, Shinji; Hershey, John R.
      Patent No.: 10,176,799
      Issue Date: Jan 8, 2019
    • Title: "Method and System for Role Dependent Context Sensitive Spoken and Textual Language Understanding with Neural Networks"
      Inventors: Hori, Chiori; Hori, Takaaki; Watanabe, Shinji; Hershey, John R.
      Patent No.: 9,842,106
      Issue Date: Dec 12, 2017
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