175 citations · 706 across the 54 of their papers we have counts for
21 papers · 1 filter
Explaining Speaker and Spoof Embeddings via Probing
Xuechen Liu, Junichi Yamagishi, Md Sahidullah +1
This study investigates the explainability of embedding representations, specifically those used in modern audio spoofing detection systems based on deep neural networks, known as…
Beyond Silence: Bias Analysis through Loss and Asymmetric Approach in Audio Anti-Spoofing
Hye-jin Shim, Md Sahidullah, Jee-weon Jung +2
Current trends in audio anti-spoofing detection research strive to improve models' ability to generalize across unseen attacks by learning to identify a variety of spoofing artifac…
Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddings
Shakeel A. Sheikh, Md Sahidullah, Fabrice Hirsch +1
The adoption of advanced deep learning architectures in stuttering detection (SD) tasks is challenging due to the limited size of the available datasets. To this end, this work int…
Speaker-Aware Anti-Spoofing
Xuechen Liu, Md Sahidullah, Kong Aik Lee +1
We address speaker-aware anti-spoofing, where prior knowledge of the target speaker is incorporated into a voice spoofing countermeasure (CM). In contrast to the frequently used sp…
Distilling Multi-Level X-vector Knowledge for Small-footprint Speaker Verification
Xuechen Liu, Md Sahidullah, Tomi Kinnunen
Even though deep speaker models have demonstrated impressive accuracy in speaker verification tasks, this often comes at the expense of increased model size and computation time, p…
Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning
Shakeel A. Sheikh, Md Sahidullah, Fabrice Hirsch +1
Stuttering is a neuro-developmental speech impairment characterized by uncontrolled utterances (interjections) and core behaviors (blocks, repetitions, and prolongations), and is c…