TBDM-Net: Bidirectional Dense Networks with Gender Information for Speech Emotion Recognition
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929500762144768 |
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| author | Striletchi, Vlad Striletchi, Cosmin Stan, Adriana |
| author_facet | Striletchi, Vlad Striletchi, Cosmin Stan, Adriana |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10056 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TBDM-Net: Bidirectional Dense Networks with Gender Information for Speech Emotion Recognition Striletchi, Vlad Striletchi, Cosmin Stan, Adriana Audio and Speech Processing Sound 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 |
| title | TBDM-Net: Bidirectional Dense Networks with Gender Information for Speech Emotion Recognition |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2409.10056 |