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Chiori Hori

Chiori Hori
Chiori's Quick Links

  • 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 K. 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
    •  

    See All News & Events for Chiori

  • Research Highlights

  • Internships with Chiori

    • SA1686: Multimodal scene understanding

      We are looking for a graduate student interested in helping advance the field of multi-modal scene understanding, with a focus on detailed captioning of a scene using natural language. The intern will collaborate with MERL researchers to derive and implement new models and optimization methods, conduct experiments, and prepare results for publication. The ideal candidate would be a senior Ph.D. student with experience in deep learning for audio-visual, signal, and natural language processing. The expected duration of the internship is 3-6 months, and start date is flexible. This internship is preferred to be onsite at MERL, but may be done remotely where you live if the COVID pandemic makes it necessary.

    See All Internships at MERL

  • MERL Publications

    •  Shah, A.P., Geng, S., Gao, P., Cherian, A., Hori, T., Marks, T.K., Le Roux, J., Hori, C., "Audio-Visual Scene-Aware Dialog and Reasoning Using Audio-Visual Transformers with Joint Student-Teacher Learning", arXiv, October 2021.
      BibTeX
      • @inproceedings{Shah2021oct,
      • author = {Shah, Ankit Parag and Geng, Shijie and Gao, Peng and Cherian, Anoop and Hori, Takaaki and Marks, Tim K. and Le Roux, Jonathan and Hori, Chiori},
      • title = {Audio-Visual Scene-Aware Dialog and Reasoning Using Audio-Visual Transformers with Joint Student-Teacher Learning},
      • booktitle = {arXiv},
      • year = 2021,
      • month = oct
      • }
    •  Hori, T., Moritz, N., Hori, C., Le Roux, J., "Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers", Annual Conference of the International Speech Communication Association (Interspeech), DOI: 10.21437/​Interspeech.2021-1643, August 2021, pp. 2097-2101.
      BibTeX TR2021-100 PDF
      • @inproceedings{Hori2021aug3,
      • author = {Hori, Takaaki and Moritz, Niko and Hori, Chiori and Le Roux, Jonathan},
      • title = {Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformers},
      • booktitle = {Annual Conference of the International Speech Communication Association (Interspeech)},
      • year = 2021,
      • pages = {2097--2101},
      • month = aug,
      • doi = {10.21437/Interspeech.2021-1643},
      • url = {https://www.merl.com/publications/TR2021-100}
      • }
    •  Hori, C., Hori, T., Le Roux, J., "Optimizing Latency for Online Video Captioning Using Audio-VisualTransformers", Annual Conference of the International Speech Communication Association (Interspeech), DOI: 10.21437/​Interspeech.2021-1975, August 2021, pp. 586–590.
      BibTeX TR2021-093 PDF
      • @inproceedings{Hori2021aug2,
      • author = {Hori, Chiori and Hori, Takaaki and Le Roux, Jonathan},
      • title = {Optimizing Latency for Online Video Captioning Using Audio-VisualTransformers},
      • booktitle = {Annual Conference of the International Speech Communication Association (Interspeech)},
      • year = 2021,
      • pages = {586–590},
      • month = aug,
      • publisher = {ISCA},
      • doi = {10.21437/Interspeech.2021-1975},
      • url = {https://www.merl.com/publications/TR2021-093}
      • }
    •  Kim, S., Galley, M., Gunasekara, C., Lee, S., Atkinson, A., Peng, B., Schulz, H., Gao, J., Li, J., Adada, M., Huang, M., Lastras, L., Kummerfeld, J.K., Lasecki, W.S., Hori, C., Cherian, A., Marks, T.K., Rastogi, A., Zang, X., Sunkara, S., Gupta, R., "Overview of the Eighth Dialog System Technology Challenge: DSTC8", IEEE/ACM Transactions on Audio, Speech, and Language Processing, DOI: 10.1109/​TASLP.2021.3078368, May 2021.
      BibTeX TR2021-064 PDF
      • @article{Kim2021may,
      • author = {Kim, Seokhwan and Galley, Michel and Gunasekara, Chulaka and Lee, Sungjin and Atkinson, Adam and Peng, Baolin and Schulz, Hannes and Gao, Jianfeng and Li, Jinchao and Adada, Mahmoud and Huang, Minlie and Lastras, Luis and Kummerfeld, Jonathan K. and Lasecki, Walter S. and Hori, Chiori and Cherian, Anoop and Marks, Tim K. and Rastogi, Abhinav and Zang, Xiaoxue and Sunkara, Srinivas and Gupta, Raghav},
      • title = {Overview of the Eighth Dialog System Technology Challenge: DSTC8},
      • journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
      • year = 2021,
      • month = may,
      • doi = {10.1109/TASLP.2021.3078368},
      • issn = {2329-9290},
      • url = {https://www.merl.com/publications/TR2021-064}
      • }
    •  Hori, C., Tsuchiya, M., Chen, S., Cherian, A., Hori, T., Harsham, B.A., Marks, T.K., Le Roux, J., Sullivan, A., Vetro, A., "マルチモーダルセンシング情報に基づくScene-aware Interaction 技術", Society of Automotive Engineers of Japan, Vol. 75, No. 5, pp. 66-71, May 2021.
      BibTeX TR2021-042 PDF
      • @article{Hori2021may,
      • author = {Hori, Chiori and Tsuchiya, Masato and Chen, Siheng and Cherian, Anoop and Hori, Takaaki and Harsham, Bret A. and Marks, Tim K. and Le Roux, Jonathan and Sullivan, Alan and Vetro, Anthony},
      • title = {マルチモーダルセンシング情報に基づくScene-aware Interaction 技術},
      • journal = {Society of Automotive Engineers of Japan},
      • year = 2021,
      • volume = 75,
      • number = 5,
      • pages = {66--71},
      • month = may,
      • url = {https://www.merl.com/publications/TR2021-042}
      • }
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  • MERL Issued Patents

    • Title: "Method and System for Multi-Label Classification"
      Inventors: Hori, Takaaki; Hori, Chiori; Watanabe, Shinji; Hershey, John R.; Harsham, Bret A.; Le Roux, Jonathan
      Patent No.: 11,086,918
      Issue Date: Aug 10, 2021
    • Title: "Position Estimation Under Multipath Transmission"
      Inventors: Kim, Kyeong-Jin; Orlik, Philip V.; Hori, Chiori
      Patent No.: 11,079,495
      Issue Date: Aug 3, 2021
    • 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
    See All Patents for MERL