  Multimodal emotion recognition from speech is an important area in affective
computing. Fusing multiple data modalities and learning representations with
limited amounts of labeled data is a challenging task. In this paper, we
explore the use of modality-specific "BERT-like" pretrained Self Supervised
Learning (SSL) architectures to represent both speech and text modalities for
the task of multimodal speech emotion recognition. By conducting experiments on
three publicly available datasets (IEMOCAP, CMU-MOSEI, and CMU-MOSI), we show
that jointly fine-tuning "BERT-like" SSL architectures achieve state-of-the-art
(SOTA) results. We also evaluate two methods of fusing speech and text
modalities and show that a simple fusion mechanism can outperform more complex
ones when using SSL models that have similar architectural properties to BERT.

Audio and Speech Processing;Computation and Language;Sound