TBDM-Net: Bidirectional Dense Networks with Gender Information for Speech Emotion Recognition
<|reference_start|>TBDM-Net: Bidirectional Dense Networks with Gender Information for Speech Emotion Recognition: 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<|reference_end|>
@article{striletchi2024tbdm-net:,
    title={TBDM-Net: Bidirectional Dense Networks with Gender Information for
  Speech Emotion Recognition},
    author={Vlad Striletchi and Cosmin Striletchi and Adriana Stan},
    journal={arXiv preprint arXiv:2409.10056},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10056},
    primaryClass={eess.AS cs.SD}
}