528 citations · 541 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
Xi-Vector Embedding for Speaker Recognition
Kong Aik Lee, Qiongqiong Wang, Takafumi Koshinaka
We present a Bayesian formulation for deep speaker embedding, wherein the xi-vector is the Bayesian counterpart of the x-vector, taking into account the uncertainty estimate. On th…
Using Multi-Resolution Feature Maps with Convolutional Neural Networks for Anti-Spoofing in ASV
Qiongqiong Wang, Kong Aik Lee, Takafumi Koshinaka
This paper presents a simple but effective method that uses multi-resolution feature maps with convolutional neural networks (CNNs) for anti-spoofing in automatic speaker verificat…
A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition
Qiongqiong Wang, Koji Okabe, Kong Aik Lee +1
This paper proposes a generalized framework for domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA) in speaker recognition. It not only includes several existing…
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
Kong Aik Lee, Ville Hautamaki, Tomi Kinnunen +43
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which…
Attentive Statistics Pooling for Deep Speaker Embedding
Koji Okabe, Takafumi Koshinaka, Koichi Shinoda
This paper proposes attentive statistics pooling for deep speaker embedding in text-independent speaker verification. In conventional speaker embedding, frame-level features are av…