{Kenneth Liang Church Renjie Huang Jiahong Zheng Xingyu Yuan Cai}
Abstract
Speech emotion recognition (SER) classifies speech into emotion categories such as: Happy, Angry, Sad and Neutral. Recently , deep learning has been applied to the SER task. This paper proposes a multi-task learning (MTL) framework to simultaneously perform speech-to-text recognition and emotion classification, with an end-to-end deep neural model based on wav2vec-2.0. Experiments on the IEMOCAP benchmark show that the proposed method achieves the state-of-the-art performance on the SER task. In addition, an ablation study establishes the effectiveness of the proposed MTL framework.
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| speech-emotion-recognition-on-iemocap | SER with MTL | F1: - UA CV: 0.7815 |
Build AI with AI
From idea to launch — accelerate your AI development with free AI co-coding, out-of-the-box environment and best price of GPUs.
AI Co-coding
Ready-to-use GPUs
Best Pricing
Hyper Newsletters
Subscribe to our latest updates
We will deliver the latest updates of the week to your inbox at nine o'clock every Monday morning
Powered by MailChimp