Enhancing Embedded Space with Low–Level Features for Speech Emotion Recognition
This work proposes an approach that uses a feature space by combining the representation obtained in the unsupervised learning process and manually selected features defining the prosody of the utterances. In the experiments, we used two time-frequency representations (Mel and CQT spectrograms) and...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/5/2598 |