# Multimodal and Multi-view Models for Emotion Recognition

## TL;DR

This paper explores combining lexical and acoustic data for emotion recognition, proposing a multi-view learning approach that enhances acoustic-only models by leveraging semantic information from multimodal training, resulting in improved performance.

## Contribution

It introduces a multi-view learning framework that transfers semantic knowledge from multimodal models to acoustic-only models, addressing practical deployment constraints.

## Key findings

- Multimodal models outperform previous state-of-the-art on USC-IEMOCAP.
- Multi-view training significantly improves acoustic-only model performance.
- Contrastive loss effectively transfers semantic information to acoustic models.

## Abstract

Studies on emotion recognition (ER) show that combining lexical and acoustic information results in more robust and accurate models. The majority of the studies focus on settings where both modalities are available in training and evaluation. However, in practice, this is not always the case; getting ASR output may represent a bottleneck in a deployment pipeline due to computational complexity or privacy-related constraints. To address this challenge, we study the problem of efficiently combining acoustic and lexical modalities during training while still providing a deployable acoustic model that does not require lexical inputs. We first experiment with multimodal models and two attention mechanisms to assess the extent of the benefits that lexical information can provide. Then, we frame the task as a multi-view learning problem to induce semantic information from a multimodal model into our acoustic-only network using a contrastive loss function. Our multimodal model outperforms the previous state of the art on the USC-IEMOCAP dataset reported on lexical and acoustic information. Additionally, our multi-view-trained acoustic network significantly surpasses models that have been exclusively trained with acoustic features.