  Multimodal language analysis is a burgeoning field of NLP that aims to
simultaneously model a speaker's words, acoustical annotations, and facial
expressions. In this area, lexicon features usually outperform other modalities
because they are pre-trained on large corpora via Transformer-based models.
Despite their strong performance, training a new self-supervised learning (SSL)
Transformer on any modality is not usually attainable due to insufficient data,
which is the case in multimodal language learning. This work proposes a
Transformer-Based Speech-Prefixed Language Model called TEASEL to approach the
mentioned constraints without training a complete Transformer model. TEASEL
model includes speech modality as a dynamic prefix besides the textual modality
compared to a conventional language model. This method exploits a conventional
pre-trained language model as a cross-modal Transformer model. We evaluated
TEASEL for the multimodal sentiment analysis task defined by CMU-MOSI dataset.
Extensive experiments show that our model outperforms unimodal baseline
language models by 4% and outperforms the current multimodal state-of-the-art
(SoTA) model by 1% in F1-score. Additionally, our proposed method is 72%
smaller than the SoTA model.
