| Paper ID | SPE-23.1 |
| Paper Title |
META-LEARNING FOR LOW-RESOURCE SPEECH EMOTION RECOGNITION |
| Authors |
Suransh Chopra, MIDAS, IIIT-Delhi, India; Puneet Mathur, University of Maryland, College Park, United States; Ramit Sawhney, MIDAS, IIIT-Delhi, India; Rajiv Ratn Shah, MIDAS, IIIT Delhi, India |
| Session | SPE-23: Speech Emotion 1: Speech Emotion Recognition |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-ANLS] Speech Analysis |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
While emotion recognition is a well-studied task, it remains unexplored to a large extent in cross-lingual settings. Speech Emotion Recognition (SER) in low-resource languages poses difficulties as existing approaches for knowledge transfer do not generalize seamlessly. Probing the learning process of generalized representations across languages, we propose a meta-learning approach for low-resource speech emotion recognition. The proposed approach achieves fast adaptation on a number of unseen target languages simultaneously. We evaluate the Model Agnostic Meta-Learning (MAML) algorithm on three low-resource target languages - Persian, Italian, and Urdu. We empirically demonstrate that our proposed method - MetaSER, considerably outperforms multitask and transfer learning-based methods for speech emotion recognition task, and discuss the benefits, efficiency, and challenges of MetaSER on limited data settings. |
