Benchmarking Cross-Domain Audio-Visual Deception Detection

arXiv:2405.06995v1 Announce Type: cross
Abstract: Automated deception detection is crucial for assisting humans in accurately assessing truthfulness and identifying deceptive behavior. Conventional contact-based techniques, like polygraph devices, rely on physiological signals to determine the authenticity of an individual's statements. Nevertheless, recent developments in automated deception detection have demonstrated that multimodal features derived from both audio and video modalities may outperform human observers on publicly available datasets. Despite these positive findings, the generalizability of existing audio-visual deception detection approaches across different …

arxiv audio benchmarking cs.cv cs.mm cs.sd deception detection domain eess.as type visual