MuDD dataset plus GSR-guided progressive distillation with dynamic routing achieves state-of-the-art non-contact deception detection and concealed-digit identification.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
method 1polarities
use method 1representative citing papers
DM-ASR reformulates multi-speaker ASR as multi-turn dialogue generation conditioned on diarization results, achieving competitive benchmark performance with relatively small models and limited data.
AT-ADD introduces standardized tracks and datasets for evaluating audio deepfake detectors on speech under real-world conditions and on diverse unknown audio types to promote generalization beyond speech-centric methods.
citing papers explorer
-
MuDD: A Multimodal Deception Detection Dataset and GSR-Guided Progressive Distillation for Non-Contact Deception Detection
MuDD dataset plus GSR-guided progressive distillation with dynamic routing achieves state-of-the-art non-contact deception detection and concealed-digit identification.
-
DM-ASR: Diarization-aware Multi-speaker ASR with Large Language Models
DM-ASR reformulates multi-speaker ASR as multi-turn dialogue generation conditioned on diarization results, achieving competitive benchmark performance with relatively small models and limited data.
-
AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan
AT-ADD introduces standardized tracks and datasets for evaluating audio deepfake detectors on speech under real-world conditions and on diverse unknown audio types to promote generalization beyond speech-centric methods.