4 accepted papers
Audio deepfake detection (ADD) aims to verify the authenticity of audio. However, its performance declines sharply when facing significant domain discrepancies caused by unknown datasets. Unsupervised domain adaptation (UDA) has been applied to mitigate domain mismatch. However, as generative models…
In the tasks of domain adaptation (DA) for speech emotion recognition (SER), self-supervised learning (SSL) algorithms could effectively explore domain and structural information from target domain samples, thereby mitigating domain discrepancies. However, in a general setting, when the target domai…
Optimal transport (OT) can be applied to cross-domain adaptation in speaker verification (SV) by converting speakers' probability distributions from source to target domains. However, in scenarios involving over-massive categories (speakers) or difficult samples in discrimination, OT often has diffi…
Automatic speaker verification (ASV) systems, which determine whether two speeches are from the same speaker, mainly focus on verification accuracy while ignoring inference speed. However, in real applications, both inference speed and verification accuracy are essential. This study proposes cross-s…