用双向膨胀卷积与性别信息提升语音情绪识别准确率
TBDM-Net: Bidirectional Dense Networks with Gender Information for Speech Emotion Recognition
- 采用双向膨胀卷积层的密集连接结构，捕捉多尺度语音特征
- 在6个公开数据集上平均准确率达78.9%，优于多数基线模型
- 加入性别标签可提升情绪识别效果，适合需个性化服务场景
本文提出一种面向语音情绪识别（SER）的新型深度神经网络架构——TBDM-Net：时序感知的双向密集多尺度网络。该架构利用多层双向膨胀卷积之间的密集连接，通过线性核动态融合各层输出，生成最终的情绪分类结果。我们在六个广泛认可的单模态语音情绪识别数据集上对TBDM-Net进行了全面性能评估，包含消融实验。此外，研究了在输入或预测阶段加入真实或预测性别标签对情绪识别的影响。实验表明，引入性别信息可进一步提升性能。代码已开源：https://github.com/adrianastan/tbdm-net
原文摘要 · Abstract (English)
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
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