Pre-Trained Model Representations and Their Robustness Against Noise for Speech Emotion Analysis
Author:
Affiliation:
1. Apple
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096968.pdf?arnumber=10096968
Reference21 articles.
1. Jointly Fine-Tuning “BERT-Like” Self Supervised Models to Improve Multimodal Speech Emotion Recognition
2. Representation learning through crossmodal conditional teacher-student training for speech emotion recognition;srinivasan,2021
3. Speech Emotion: Investigating Model Representations, Multi-Task Learning and Knowledge Distillation
4. Emotion Recognition from Speech Using wav2vec 2.0 Embeddings
5. BERT: Pre-training of deep bidirectional transformers for language understanding;devlin;Proceedings of NAACL-HLT,2019
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1. Pretraining Enhanced RNN Transducer;CAAI Artificial Intelligence Research;2024-12
2. Investigating Salient Representations and Label Variance in Dimensional Speech Emotion Analysis;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14
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