Reading To Listen at the Cocktail Party: Multi-Modal Speech Separation

Akam Rahimi, Triantafyllos Afouras, Andrew Zisserman; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 10493-10502

The goal of this paper is speech separation and enhancement in multi-speaker and noisy environments using a combination of different modalities. Previous works have shown good performance when conditioning on temporal or static visual evidence such as synchronised lip movements or face identity. In this paper we present a unified framework for multi-modal speech separation and enhancement based on synchronous or asynchronous cues. To that end we make the following contributions: (i) we design a modern Transformer-based architecture which inputs and outputs raw waveforms and is tailored to fuse different modalities to solve the speech separation task; (ii) we propose conditioning on the text content of a sentence alone or in combination with visual information; (iii) we demonstrate the robustness of our model to audio-visual synchronisation offsets; and, (iv) we obtain state-of-the art performance on the well-established benchmark datasets LRS2 and LRS3.