352 citations · 849 across the 13 of their papers we have counts for
4 papers · 1 filter
Don't speak too fast: The impact of data bias on self-supervised speech models
Yen Meng, Yi-Hui Chou, Andy T. Liu +1
Self-supervised Speech Models (S3Ms) have been proven successful in many speech downstream tasks, like ASR. However, how pre-training data affects S3Ms' downstream behavior remains…
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning
Haibin Wu, Xu Li, Andy T. Liu +3
Previous works have shown that automatic speaker verification (ASV) is seriously vulnerable to malicious spoofing attacks, such as replay, synthetic speech, and recently emerged ad…
SUPERB: Speech processing Universal PERformance Benchmark
Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang +17
Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large…
Adversarial defense for automatic speaker verification by cascaded self-supervised learning models
Haibin Wu, Xu Li, Andy T. Liu +3
Automatic speaker verification (ASV) is one of the core technologies in biometric identification. With the ubiquitous usage of ASV systems in safety-critical applications, more and…