Persian Speech Emotion Recognition by Fine-Tuning Transformers

TL;DR

This paper explores the use of fine-tuned transformer models for Persian speech emotion recognition, achieving significant accuracy improvements by leveraging multilingual training and different input representations.

Contribution

It introduces transformer-based models for Persian speech emotion recognition and demonstrates their effectiveness with accuracy improvements over previous methods.

Findings

Accuracy increased from 65% to 80% on the shEMO dataset.

Multilingual fine-tuning with English and Persian data further improved accuracy to 82%.

Models based on spectrograms and raw audio both showed significant performance gains.

Abstract

Given the significance of speech emotion recognition, numerous methods have been developed in recent years to create effective and efficient systems in this domain. One of these methods involves the use of pretrained transformers, fine-tuned to address this specific problem, resulting in high accuracy. Despite extensive discussions and global-scale efforts to enhance these systems, the application of this innovative and effective approach has received less attention in the context of Persian speech emotion recognition. In this article, we review the field of speech emotion recognition and its background, with an emphasis on the importance of employing transformers in this context. We present two models, one based on spectrograms and the other on the audio itself, fine-tuned using the shEMO dataset. These models significantly enhance the accuracy of previous systems, increasing it from approximately 65% to 80% on the mentioned dataset. Subsequently, to investigate the effect of multilinguality on the fine-tuning process, these same models are fine-tuned twice. First, they are fine-tuned using the English IEMOCAP dataset, and then they are fine-tuned with the Persian shEMO dataset. This results in an improved accuracy of 82% for the Persian emotion recognition system. Keywords: Persian Speech Emotion Recognition, shEMO, Self-Supervised Learning