Recognizing More Emotions with Less Data Using Self-supervised Transfer Learning

Understand

We propose a novel transfer learning method for speech emotion recognition allowing us to obtain promising results when only few training data is available.

• With as low as 125 examples per emotion class, we were able to reach a higher accuracy than a strong baseline trained on 8 times more data.
• Our method leverages knowledge contained in pre-trained speech representations extracted from models trained on a more general self-supervised task which doesn't require human annotations, such as the wav2vec model.
• We provide detailed insights on the benefits of our approach by varying the training data size, which can help labeling teams to work more efficiently.