  Multi-modal cues, including spatial information, facial expression and
voiceprint, are introduced to the speech separation and speaker extraction
tasks to serve as complementary information to achieve better performance.
However, the introduction of these cues brings about an increasing number of
parameters and model complexity, which makes it harder to deploy these models
on resource-constrained devices. In this paper, we alleviate the aforementioned
problem by proposing a Lightweight Multi-modal framework for Speaker Extraction
(LiMuSE). We propose to use GC-equipped TCN, which incorporates Group
Communication (GC) and Temporal Convolutional Network (TCN) in the Context
Codec module, the audio block and the fusion block. The experiments on the
MC_GRID dataset demonstrate that LiMuSE achieves on par or better performance
with a much smaller number of parameters and less model complexity. We further
investigate the impacts of the quantization of LiMuSE. Our code and dataset are
provided.
