  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.
