MDG:Multilingual Co-speech Gesture Generation with Low-level Audio Representation and Diffusion Models

Co-speech gesture generation is crucial for enhancing human-computer interaction. Most existing research focuses on English, overlooking multilingual speech and the ability to generalize across languages. Additionally, these studies often rely on pre-trained audio feature extraction models, insufficient for non-English languages. To address these issues, we introduce MDG, a novel diffusion model-based method for speech-driven gesture generation using a multilingual dataset. Our approach incorporates Language ID to distinguish between languages and utilizes low-level audio features to improve multilingual generalization. Extensive experiments demonstrate that MDG significantly outperforms baseline models in multilingual co-speech gesture generation. For more details, please visit our demo page<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.

Authors

Institutions

Publication Details

Published
2024-08-04
DOI
https://doi.org/10.1109/ialp63756.2024.10661182
Primary Topic
Music and Audio Processing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MDG:Multilingual Co-speech Gesture Generation with Low-level Audio Representation and Diffusion Models

Feilong Bao, Jie Yang
Music and Audio Processing
article

MDG:Multilingual Co-speech Gesture Generation with Low-level Audio Representation and Diffusion Models

Feilong Bao, Jie Yang
article en

Abstract

Co-speech gesture generation is crucial for enhancing human-computer interaction. Most existing research focuses on English, overlooking multilingual speech and the ability to generalize across languages. Additionally, these studies often rely on pre-trained audio feature extraction models, insufficient for non-English languages. To address these issues, we introduce MDG, a novel diffusion model-based method for speech-driven gesture generation using a multilingual dataset. Our approach incorporates Language ID to distinguish between languages and utilizes low-level audio features to improve multilingual generalization. Extensive experiments demonstrate that MDG significantly outperforms baseline models in multilingual co-speech gesture generation. For more details, please visit our demo page<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>.

Inner Mongolia University (CN)
Openalex Percentile: Top 84%
Music and Audio Processing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

MDG:Multilingual Co-speech Gesture Generation with Low-level Audio Representation and Diffusion Models — Feilong Bao, Jie Yang · (2024) | TGRS Research Map | TGRS