In this work, we present, AVR application for audio-visual humor detection. While humor detection has traditionally centered around textual analysis, recent advancements have spotlighted multimodal approaches. However, these methods lean on textual cues as a modality, necessitating the use of ASR systems for transcribing the audio-data. This heavy reliance on ASR accuracy can pose challenges in real-world applications. To address this bottleneck, we propose an innovative audio-visual humor detection system that circumvents textual reliance, eliminating the need for ASR models. Instead, the proposed approach hinges on the intricate interplay between audio and visual content for effective humor detection.
@article{arxiv.2406.10448,
title = {AVR: Synergizing Foundation Models for Audio-Visual Humor Detection},
author = {Sarthak Sharma and Orchid Chetia Phukan and Drishti Singh and Arun Balaji Buduru and Rajesh Sharma},
journal= {arXiv preprint arXiv:2406.10448},
year = {2024}
}
Comments
Accepted to INTERSPEECH 2024 Show & Tell Demonstrations