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David Gard

2 accepted papers

2023

Adapting a Self-Supervised Speech Representation for Noisy Speech Emotion Recognition by Using Contrastive Teacher-Student Learning

ICASSP 2023accepted

Studies have shown high performance in the speech emotion recognition (SER) task by fine-tuning a self-supervised speech representation model. Although this model can provide emotionally discriminative embedding in clean conditions, adapting it to a noisy target environment is still required when de…

Cited by 0SourceScholar
2022

Not All Features are Equal: Selection of Robust Features for Speech Emotion Recognition in Noisy Environments

ICASSP 2022accepted

Speech emotion recognition (SER) system deployed in real-world applications often encounters noisy speech. While most noise compensation techniques consider all acoustic features to have equal impact on the SER model, some acoustic features may be more sensitive to noisy conditions. This paper inves…

Cited by 31SourceScholar