TBDM-Net: Bidirectional Dense Networks with Gender Information for Speech Emotion Recognition

TL;DR

TBDM-Net is a novel deep neural network architecture for Speech Emotion Recognition that leverages bidirectional dense convolutions and gender information to improve emotion classification accuracy across multiple datasets.

Contribution

The paper introduces TBDM-Net, a new architecture combining bidirectional dilated convolutions with gender-informed features for enhanced speech emotion recognition.

Findings

TBDM-Net outperforms existing models on six SER datasets.

Gender information improves emotion prediction accuracy.

Ablation studies highlight the importance of dense connections and gender features.

Abstract

This paper presents a novel deep neural network-based architecture tailored for Speech Emotion Recognition (SER). The architecture capitalises on dense interconnections among multiple layers of bidirectional dilated convolutions. A linear kernel dynamically fuses the outputs of these layers to yield the final emotion class prediction. This innovative architecture is denoted as TBDM-Net: Temporally-Aware Bi-directional Dense Multi-Scale Network. We conduct a comprehensive performance evaluation of TBDM-Net, including an ablation study, across six widely-acknowledged SER datasets for unimodal speech emotion recognition. Additionally, we explore the influence of gender-informed emotion prediction by appending either golden or predicted gender labels to the architecture's inputs or predictions. The implementation of TBDM-Net is accessible at: https://github.com/adrianastan/tbdm-net