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Otsuka Lab (Multimodal Information Processing Lab)
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A lab that explores people's inner states from gaze and head movements during conversation.
AboutAbout the lab
Toward AI with the social intelligence to read the room.
The Otsuka Lab aims to realize artificial intelligence with "social intelligence" that can understand even subtle nuances of the mind, such as "ishin-denshin" (tacit mutual understanding) and "reading the room."
The lab studies technologies that integrate data such as images, speech, language, and motion observed with cameras, microphones, and sensors, and automatically recognize and estimate people's emotions, intentions, personalities, and relationships with others.
Its research covers observing human behavior in conversations and meetings, building mathematical models of behavior and inner states and recognizing them automatically, and applied systems such as remote meetings.
Students learn techniques such as deep learning and Bayesian models through a "think while building" approach, hands-on developing programs and systems.
Key terms
- function spectrum
- A vector representing the perceived strength of each of the multiple functions of gaze. It is expressed as the proportion of raters who recognized each function.
- feature expansion-contraction mechanism
- A mechanism that repeatedly expands and compresses input data to promote feature learning from time series.
- subjective impression
- Impressions such as enjoyment or difficulty as felt by the participants themselves. They are assessed through self-reports after the dialogue.
ResearchResearch themes
The Otsuka Lab studies technologies that integrate images, speech, language, motion, and other data obtained with cameras, microphones, and sensors, and automatically recognize and estimate people's internal states and their relationships with others. The lab aims to create the principles of artificial intelligence that can promote dialogue and empathy between people, and between people and machines, and support problem solving and consensus building.
*Illustrative imageAnalyzing the multiple functions of gaze
Gaze during conversation has functions such as observing the other person and regulating the flow of the dialogue. The lab applies matrix factorization to the function spectra of speakers and listeners to analyze the functions of gaze-mediated interaction.
*Illustrative imagePredicting impressions from head movements and facial expressions
The lab proposes a model that predicts participants' subjective impressions, such as enjoyment and difficulty, using the functions of head movements and facial expressions during dialogue as cues. Neural networks detect the functions, and random forest regression makes the predictions.
Recognizing the functions of head movements
Head movements during conversation serve various functions, such as backchannels and signaling agreement. To improve recognition performance, the lab proposes a feature expansion-contraction mechanism that repeatedly expands and compresses data.
*Illustrative imageThe relationship between head movement functions and subjective impressions
Using features related to the functions of head movements, the lab proposes a model that predicts subjective impressions in multi-party conversations, such as a good atmosphere, enjoyment, motivation, and concentration. This was carried out as joint research with Nippon Telegraph and Telephone Corporation.
MembersFaculty
The Otsuka Lab's members consist of faculty, master's students, and undergraduate students.As of 2026
- ProfessorKazuhiro Otsuka
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Eye-tracking setup” Naomi Nota / CC BY-SA 2.0 (cropped)
- Analyzing the multiple functions of gaze: “EYE-SYNC eye-tracking analyzer” Z22 / CC BY-SA 4.0 (cropped)
- Predicting impressions from head movements and facial expressions: “Body Motion Capture” Raíssa Ruschel / CC BY 2.0 (cropped)
- The relationship between head movement functions and subjective impressions: “Petra, People in conversation, Jordan” Vyacheslav Argenberg / CC BY 4.0 (cropped)
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