352 citations · 849 across the 13 of their papers we have counts for
5 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…
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…
TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech
Andy T. Liu, Shang-Wen Li, Hung-yi Lee
We introduce a self-supervised speech pre-training method called TERA, which stands for Transformer Encoder Representations from Alteration. Recent approaches often learn by using…
Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning
Haibin Wu, Andy T. Liu, Hung-yi Lee
High-performance anti-spoofing models for automatic speaker verification (ASV), have been widely used to protect ASV by identifying and filtering spoofing audio that is deliberatel…
Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders
Andy T. Liu, Shu-wen Yang, Po-Han Chi +2
We present Mockingjay as a new speech representation learning approach, where bidirectional Transformer encoders are pre-trained on a large amount of unlabeled speech. Previous spe…