  Training on multiple modalities of input can augment the capabilities of a
language model. Here, we ask whether such a training regime can improve the
quality and efficiency of these systems as well. We focus on text--audio and
introduce Whisbert, which is inspired by the text--image approach of FLAVA
(Singh et al., 2022). In accordance with Babylm guidelines (Warstadt et al.,
2023), we pretrain Whisbert on a dataset comprising only 100 million words plus
their corresponding speech from the word-aligned version of the People's Speech
dataset (Galvez et al., 2021). To assess the impact of multimodality, we
compare versions of the model that are trained on text only and on both audio
and text simultaneously. We find that while Whisbert is able to perform well on
multimodal masked modeling and surpasses the Babylm baselines in most benchmark
tasks, it struggles to optimize its complex objective and outperform its
text-only Whisbert baseline.

Computation and Language;Artificial Intelligence