11 citations · 31 across the 8 of their papers we have counts for
8 papers
Self-Supervised Learning for Speaker Recognition: A study and review
Theo Lepage, Reda Dehak
Deep learning models trained in a supervised setting have revolutionized audio and speech processing. However, their performance inherently depends on the quantity of human-annotat…
SSPS: Self-Supervised Positive Sampling for Robust Self-Supervised Speaker Verification
Theo Lepage, Reda Dehak
Self-Supervised Learning (SSL) has led to considerable progress in Speaker Verification (SV). The standard framework uses same-utterance positive sampling and data-augmentation to…
Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling
Theo Lepage, Reda Dehak
Recent developments in Self-Supervised Learning (SSL) have demonstrated significant potential for Speaker Verification (SV), but closing the performance gap with supervised systems…
Exploring WavLM Back-ends for Speech Spoofing and Deepfake Detection
Theophile Stourbe, Victor Miara, Theo Lepage +1
This paper describes our submitted systems to the ASVspoof 5 Challenge Track 1: Speech Deepfake Detection - Open Condition, which consists of a stand-alone speech deepfake (bonafid…
Towards Supervised Performance on Speaker Verification with Self-Supervised Learning by Leveraging Large-Scale ASR Models
Victor Miara, Theo Lepage, Reda Dehak
Recent advancements in Self-Supervised Learning (SSL) have shown promising results in Speaker Verification (SV). However, narrowing the performance gap with supervised systems rema…
Additive Margin in Contrastive Self-Supervised Frameworks to Learn Discriminative Speaker Representations
Theo Lepage, Reda Dehak
Self-Supervised Learning (SSL) frameworks became the standard for learning robust class representations by benefiting from large unlabeled datasets. For Speaker Verification (SV),…
Experimenting with Additive Margins for Contrastive Self-Supervised Speaker Verification
Theo Lepage, Reda Dehak
Most state-of-the-art self-supervised speaker verification systems rely on a contrastive-based objective function to learn speaker representations from unlabeled speech data. We ex…
Label-Efficient Self-Supervised Speaker Verification With Information Maximization and Contrastive Learning
Théo Lepage, Réda Dehak
State-of-the-art speaker verification systems are inherently dependent on some kind of human supervision as they are trained on massive amounts of labeled data. However, manually a…