Audio-video fusion strategies for active speaker detection in meetings

TL;DR

This paper explores audio-visual fusion strategies using neural networks to improve active speaker detection in meetings, demonstrating that motion information and attention-based fusion significantly enhance accuracy.

Contribution

The study introduces novel fusion methods combining visual, motion, and audio data with neural networks for active speaker detection, outperforming classical approaches.

Findings

Motion information greatly improves detection performance.

Attention-based fusion reduces variability and enhances accuracy.

Visual and audio fusion outperforms single-modality systems.

Abstract

Meetings are a common activity in professional contexts, and it remains challenging to endow vocal assistants with advanced functionalities to facilitate meeting management. In this context, a task like active speaker detection can provide useful insights to model interaction between meeting participants. Motivated by our application context related to advanced meeting assistant, we want to combine audio and visual information to achieve the best possible performance. In this paper, we propose two different types of fusion for the detection of the active speaker, combining two visual modalities and an audio modality through neural networks. For comparison purpose, classical unsupervised approaches for audio feature extraction are also used. We expect visual data centered on the face of each participant to be very appropriate for detecting voice activity, based on the detection of lip and facial gestures. Thus, our baseline system uses visual data and we chose a 3D Convolutional Neural Network architecture, which is effective for simultaneously encoding appearance and movement. To improve this system, we supplemented the visual information by processing the audio stream with a CNN or an unsupervised speaker diarization system. We have further improved this system by adding visual modality information using motion through optical flow. We evaluated our proposal with a public and state-of-the-art benchmark: the AMI corpus. We analysed the contribution of each system to the merger carried out in order to determine if a given participant is currently speaking. We also discussed the results we obtained. Besides, we have shown that, for our application context, adding motion information greatly improves performance. Finally, we have shown that attention-based fusion improves performance while reducing the standard deviation.