# Fusion approaches for emotion recognition from speech using acoustic and text-based features

arXiv: https://arxiv.org/abs/2403.18635 · HTML: https://mlsanity.com/paper/2403.18635/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Multimodal Speech Emotion Recognition Using Audio and Text](https://mlsanity.com/paper/1810.04635/) (2018)
- [Learning Alignment for Multimodal Emotion Recognition from Speech](https://mlsanity.com/paper/1909.05645/) (2019)
- [Audio-Linguistic Embeddings for Spoken Sentences](https://mlsanity.com/paper/1902.07817/) (2019)
- [Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis](https://mlsanity.com/paper/1907.08696/) (2019)

## Abstract (arXiv)

In this paper, we study different approaches for classifying emotions from speech using acoustic and text-based features. We propose to obtain contextualized word embeddings with BERT to represent the information contained in speech transcriptions and show that this results in better performance than using Glove embeddings. We also propose and compare different strategies to combine the audio and text modalities, evaluating them on IEMOCAP and MSP-PODCAST datasets. We find that fusing acoustic and text-based systems is beneficial on both datasets, though only subtle differences are observed across the evaluated fusion approaches. Finally, for IEMOCAP, we show the large effect that the criteria used to define the cross-validation folds have on results. In particular, the standard way of creating folds for this dataset results in a highly optimistic estimation of performance for the text-based system, suggesting that some previous works may overestimate the advantage of incorporating transcriptions.