7 citations · 24 across the 13 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…
Scoring of Large-Margin Embeddings for Speaker Verification: Cosine or PLDA?
Qiongqiong Wang, Kong Aik Lee, Tianchi Liu
The emergence of large-margin softmax cross-entropy losses in training deep speaker embedding neural networks has triggered a gradual shift from parametric back-ends to a simpler c…
Task-aware Warping Factors in Mask-based Speech Enhancement
Qiongqiong Wang, Kong Aik Lee, Takafumi Koshinaka +2
This paper proposes the use of two task-aware warping factors in mask-based speech enhancement (SE). One controls the balance between speech-maintenance and noise-removal in traini…
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…
The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA
Kong Aik Lee, Qiongqiong Wang, Takafumi Koshinaka
State-of-the-art speaker recognition systems comprise an x-vector (or i-vector) speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) backend.…
Attention Mechanism in Speaker Recognition: What Does It Learn in Deep Speaker Embedding?
Qiongqiong Wang, Koji Okabe, Kong Aik Lee +2
This paper presents an experimental study on deep speaker embedding with an attention mechanism that has been found to be a powerful representation learning technique in speaker re…