7 citations · 17 across the 12 of their papers we have counts for
14 papers
Cosine Scoring with Uncertainty for Neural Speaker Embedding
Qiongqiong Wang, Kong Aik Lee
Uncertainty modeling in speaker representation aims to learn the variability present in speech utterances. While the conventional cosine-scoring is computationally efficient and pr…
Golden Gemini is All You Need: Finding the Sweet Spots for Speaker Verification
Tianchi Liu, Kong Aik Lee, Qiongqiong Wang +1
Previous studies demonstrate the impressive performance of residual neural networks (ResNet) in speaker verification. The ResNet models treat the time and frequency dimensions equa…
Disentangling Voice and Content with Self-Supervision for Speaker Recognition
Tianchi Liu, Kong Aik Lee, Qiongqiong Wang +1
For speaker recognition, it is difficult to extract an accurate speaker representation from speech because of its mixture of speaker traits and content. This paper proposes a disen…
Generalized domain adaptation framework for parametric back-end in speaker recognition
Qiongqiong Wang, Koji Okabe, Kong Aik Lee +1
State-of-the-art speaker recognition systems comprise a speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) back-end. The effectiveness of t…
Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification
Qiongqiong Wang, Kong Aik Lee, Tianchi Liu
Speech utterances recorded under differing conditions exhibit varying degrees of confidence in their embedding estimates, i.e., uncertainty, even if they are extracted using the sa…
I4U System Description for NIST SRE'20 CTS Challenge
Kong Aik Lee, Tomi Kinnunen, Daniele Colibro +23
This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge. The I4U's submission was resu…