A vector quantized masked autoencoder for speech emotion recognition. (arXiv:2304.11117v1 [cs.SD])

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) …

arxiv autoencoder challenge data deep learning deep learning techniques emotion emotions masked autoencoder paper progress recognition self-supervised learning solution space speech speech emotion supervised learning vector