Evaluating Self-Supervised Speech Representations for Speech Emotion Recognition

Evaluating Self-Supervised Speech Representations for Speech Emotion Recognition

Self-supervised learning has recently been implemented widely in speech processing areas, replacing conventional acoustic feature extraction to extract meaningful information from speech. One of the challenging applications of speech processing is to extract affective information from speech, common...

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Bibliographic Details
Main Authors: Bagus Tris Atmaja, Akira Sasou
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Self-supervised learning
speech emotion recognition
acoustic feature
speech processing
affective computing
Online Access:https://ieeexplore.ieee.org/document/9964237/

Internet

https://ieeexplore.ieee.org/document/9964237/

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