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Yu Nishimura

dblp:297/2490 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0004-3978-6917ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Interaction techniques and input · 87% Ubiquitous computing and smart environments · 13%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input › input modality
silent speech interaction
1.012026
NasoVoce: A Nose-Mounted Low-Audibility Speech Interface for Always-Available Speech Interaction · CHI 2026
Interaction techniques and input › voice interaction
speech input
1.012026
NasoVoce: A Nose-Mounted Low-Audibility Speech Interface for Always-Available Speech Interaction · CHI 2026

Methods — techniques the papers use, named apart from their topics

speech recognition · 1.0sensor fusion · 1.0
YearPublicationVenuePosition
2026 NasoVoce: A Nose-Mounted Low-Audibility Speech Interface for Always-Available Speech Interaction
abstract
Silent and whispered speech offer promise for always-available voice interaction with AI, yet existing methods struggle to balance vocabulary size, wearability, silence, and noise robustness. We present NasoVoce, a nose-bridge–mounted interface that integrates a microphone and a vibration sensor. Positioned at the nasal pads of smart glasses, it unobtrusively captures both acoustic and vibration signals. The nasal bridge, close to the mouth, allows access to bone- and skin-conducted speech and enables reliable capture of low-volume utterances such as whispered speech. While the microphone captures high-quality audio, it is highly sensitive to environmental noise. Conversely, the vibration sensor is robust to noise but yields lower signal quality. By fusing these complementary inputs, NasoVoce generates high-quality speech robust against interference. Evaluation with Whisper Large-v2, PESQ, STOI, and MUSHRA ratings confirms improved recognition and quality. NasoVoce demonstrates the feasibility of a practical interface for always-available, continuous, and discreet AI voice conversations.
Jun Rekimoto, Yu Nishimura, Bojian Yang
CHI2