# Audiovisual speaker diarization of TV series

## TL;DR

This paper introduces a multi-modal approach combining audio and video data to improve speaker diarization in TV series, especially under challenging acoustic conditions, outperforming single-modality methods.

## Contribution

It presents a novel multi-modal framework for speaker diarization in narrative films, leveraging visual cues to enhance audio-based segmentation accuracy.

## Key findings

- Multi-modal approach outperforms audio-only methods
- Effective matching of audio and video speaker partitions
- Improved diarization accuracy in TV series scenes

## Abstract

Speaker diarization may be difficult to achieve when applied to narrative films, where speakers usually talk in adverse acoustic conditions: background music, sound effects, wide variations in intonation may hide the inter-speaker variability and make audio-based speaker diarization approaches error prone. On the other hand, such fictional movies exhibit strong regularities at the image level, particularly within dialogue scenes. In this paper, we propose to perform speaker diarization within dialogue scenes of TV series by combining the audio and video modalities: speaker diarization is first performed by using each modality, the two resulting partitions of the instance set are then optimally matched, before the remaining instances, corresponding to cases of disagreement between both modalities, are finally processed. The results obtained by applying such a multi-modal approach to fictional films turn out to outperform those obtained by relying on a single modality.