Lune

UbiComp2021Top-tier venue

SonicASL: An Acoustic-based Sign Language Gesture Recognizer Using Earphones

Yincheng Jin, Yang Gao, Yanjun Zhu, Wei Wang, Jiyang Li, Seokmin Choi, Zhangyu Li, Jagmohan Chauhan, Anind K. Dey, Zhanpeng Jin

2021Year
46Citations
6Top-tier citations

Abstract

We propose SonicASL, a real-time gesture recognition system that can recognize sign language gestures on the fly, leveraging front-facing microphones and speakers added to commodity earphones worn by someone facing the person making the gestures. In a user study (N=8), we evaluate the recognition performance of various sign language gestures at both the word and sentence levels. Given 42 frequently used individual words and 30 meaningful sentences, SonicASL can achieve an accuracy of 93.8% and 90.6% for word-level and sentence-level recognition, respectively. The proposed system is tested in two real-world scenarios: indoor (apartment, office, and corridor) and outdoor (sidewalk) environments with pedestrians walking nearby. The results show that our system can provide users with an effective gesture recognition tool with high reliability against environmental factors such as ambient noises and nearby pedestrians.

Ask about this paper

Ask your agent about it.

Lune has read the top-tier papers around this one, so every answer names the papers it rests on.

Questions to start from

Your agent calls

Lunesearch_papers

Ask in Lune

Free to start. No credit card required.

lune papers get 877be7b0-6677-4d66-b2eb-b7791f0d468c

Cited by top-tier papers6

Ask how each one uses it

Related papers

Dusk over the sea between two cliffs drawn in fine vertical lines