Audio-Visual Speaker Diarization: Current Databases, Approaches and Challenges

TL;DR

A robust audio-visual speaker diarization framework adaptable to various data domains, including TV scenarios, meetings, and daily activities is developed, which will also include the proposal of an approach to lead the precise assignment of specific identities in TV scenarios where celebrities appear.

Abstract

Nowadays, the large amount of audio-visual content available has fostered the need to develop new robust automatic speaker diarization systems to analyse and characterise it. This kind of system helps to reduce the cost of doing this process manually and allows the use of the speaker information for different applications, as a huge quantity of information is present, for example, images of faces, or audio recordings. Therefore, this paper aims to address a critical area in the field of speaker diarization systems, the integration of audio-visual content of different domains. This paper seeks to push beyond current state-of-the-art practices by developing a robust audio-visual speaker diarization framework adaptable to various data domains, including TV scenarios, meetings, and daily activities. Unlike most of the existing audio-visual speaker diarization systems, this framework will also include the proposal of an approach to lead the precise assignment of specific identities in TV scenarios where celebrities appear. In addition, in this work, we have conducted an extensive compilation of the current state-of-the-art approaches and the existing databases for developing audio-visual speaker diarization.

Figures (3)

• Figure 1: Challenge of realising a framework for enhancing the audio-visual speaker diarization and identification tasks across different scenarios.
• Figure 2: Examples of Audio-Visual Speaker Diarization Databases. a-d) Speakers are always visible. e-h) Off-screen speakers are possible.
• Figure 3: Audio-Visual Speaker Diarization system.