4 citations · 4 across the 9 of their papers we have counts for
9 papers
OmniVChat: Synthesizing, Benchmarking, and Training for Native Audio-Visual Dialogue
Haolin He, Yunfei Chu, Qi Chen +15
We define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model. In OmniVChat, omni models directly and simultaneously receive au…
Omni Demand Understanding: A Benchmark for Contextual User-Intent Inference in Multimodal Interaction
Qi Chen, Yunfei Chu, Haolin He +15
Natural audio-visual interaction is emerging as an important interface for AI assistants, allowing users to communicate through speech and vision rather than carefully composed tex…
LongAudioSpan: Spanning the Duration and Depth of Audio Comprehension
Wen Huang, Yunfei Chu, Meng Gao +2
General audio comprehension now covers speech, sound, and music over durations from seconds to hours, driven by large audio-language models (LALMs) that are increasingly omni-modal…
Localizing Speech Deepfakes Beyond Transitions via Segment-Aware Learning
Yuchen Mao, Wen Huang, Yanmin Qian
Localizing partial deepfake audio, where only segments of speech are manipulated, remains challenging due to the subtle and scattered nature of these modifications. Existing approa…
A Data-Centric Approach to Generalizable Speech Deepfake Detection
Wen Huang, Yuchen Mao, Yanmin Qian
Achieving robust generalization in speech deepfake detection (SDD) remains a primary challenge, as models often fail to detect unseen forgery methods. While research has focused on…
SpeechFake: A Large-Scale Multilingual Speech Deepfake Dataset Incorporating Cutting-Edge Generation Methods
Wen Huang, Yanmei Gu, Zhiming Wang +2
As speech generation technology advances, the risk of misuse through deepfake audio has become a pressing concern, which underscores the critical need for robust detection systems.…
Generalizable Audio Deepfake Detection via Latent Space Refinement and Augmentation
Wen Huang, Yanmei Gu, Zhiming Wang +2
Advances in speech synthesis technologies, like text-to-speech (TTS) and voice conversion (VC), have made detecting deepfake speech increasingly challenging. Spoofing countermeasur…
Data-Efficient Low-Complexity Acoustic Scene Classification via Distilling and Progressive Pruning
Bing Han, Wen Huang, Zhengyang Chen +7
The goal of the acoustic scene classification (ASC) task is to classify recordings into one of the predefined acoustic scene classes. However, in real-world scenarios, ASC systems…
Prototype and Instance Contrastive Learning for Unsupervised Domain Adaptation in Speaker Verification
Wen Huang, Bing Han, Zhengyang Chen +2
Speaker verification system trained on one domain usually suffers performance degradation when applied to another domain. To address this challenge, researchers commonly use featur…