528 citations · 539 across the 7 of their papers we have counts for
8 papers
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…
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…
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…
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…
Speaker detection in the wild: Lessons learned from JSALT 2019
Paola Garcia, Jesus Villalba, Herve Bredin +21
This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios. The main focus was to tack…
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…
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…
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…