Fast Yet Effective Speech Emotion Recognition with Self-Distillation
Author:
Affiliation:
1. Leibniz University Hannover,L3S Research Center,Germany
2. Griffith University,Australia
3. Audio, & Music, Imperial College London,GLAM – Group on Language,United Kingdom
Funder
Ministry of Education
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10094895.pdf?arnumber=10094895
Reference27 articles.
1. Emotion Recognition from Speech Using wav2vec 2.0 Embeddings
2. A Comprehensive Review of Speech Emotion Recognition Systems
3. Dawn of the transformer era in speech emotion recognition: closing the valence gap;wagner,2022
4. Exploring wav2vec 2.0 fine-tuning for improved speech emotion recognition;chen,2021
5. A multimodal hierarchical approach to speech emotion recognition from audio and text
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1. GDM-depth: Leveraging global dependency modelling for self-supervised indoor depth estimation;Image and Vision Computing;2024-09
2. Speech Emotion Recognition with Distilled Prosodic and Linguistic Affect Representations;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
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