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Found 7 papers, 1 papers with code
RUSAVIC Corpus: Russian Audio-Visual Speech in Cars
We present a new audio-visual speech corpus (RUSAVIC) recorded in a car environment and designed for noise-robust speech recognition.
Audio-Visual Compound Expression Recognition Method based on Late Modality Fusion and Rule-based Decision
Our findings from the challenge demonstrate that the proposed method can potentially form a basis for developing intelligent tools for annotating audio-visual data in the context of human's basic and compound emotions.
OCEAN-AI framework with EmoFormer cross-hemiface attention approach for personality traits assessment
Psychological and neurological studies earlier suggested that a personality type can be determined by the whole face as well as by its sides.
Personality Trait Recognition
Personality Trait Recognition by Face
Cross-Language Transfer Learning using Visual Information for Automatic Sign Gesture Recognition
Automatic sign gesture recognition (GR) plays a critical role in facilitating communication between hearing-impaired individuals and the rest of society.
Ranked #5 on
Sign Language Recognition
on AUTSL
Audio-Visual Speech and Gesture Recognition by Sensors of Mobile Devices
Audio-visual speech recognition (AVSR) is one of the most promising solutions for reliable speech recognition, particularly when audio is corrupted by noise.
Ranked #1 on
Sign Language Recognition
on AUTSL
Visual Speech Recognition in a Driver Assistance System
After a comprehensive evaluation, we adapt the developed method and test it on the collected RUSAVIC corpus we recorded in-the-wild for vehicle driver.
Ranked #3 on
Lipreading
on Lip Reading in the Wild
TheRuSLan: Database of Russian Sign Language
The database includes lexical units (single words and phrases) from Russian sign language within one subject area, namely, {``}food products at the supermarket{''}, and was collected using MS Kinect 2. 0 device including both FullHD video and the depth map modes, which provides new opportunities for the lexicographical description of the Russian sign language vocabulary and enhances research in the field of automatic gesture recognition.
