  Recent years have seen remarkable progress in speech emotion recognition
(SER), thanks to advances in deep learning techniques. However, the limited
availability of labeled data remains a significant challenge in the field.
Self-supervised learning has recently emerged as a promising solution to
address this challenge. In this paper, we propose the vector quantized masked
autoencoder for speech (VQ-MAE-S), a self-supervised model that is fine-tuned
to recognize emotions from speech signals. The VQ-MAE-S model is based on a
masked autoencoder (MAE) that operates in the discrete latent space of a
vector-quantized variational autoencoder. Experimental results show that the
proposed VQ-MAE-S model, pre-trained on the VoxCeleb2 dataset and fine-tuned on
emotional speech data, outperforms an MAE working on the raw spectrogram
representation and other state-of-the-art methods in SER.
