175 citations · 494 across the 44 of their papers we have counts for
25 papers · 1 filter
Towards single integrated spoofing-aware speaker verification embeddings
Sung Hwan Mun, Hye-jin Shim, Hemlata Tak +12
This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well a…
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…
Probabilistic Back-ends for Online Speaker Recognition and Clustering
Alexey Sholokhov, Nikita Kuzmin, Kong Aik Lee +1
This paper focuses on multi-enrollment speaker recognition which naturally occurs in the task of online speaker clustering, and studies the properties of different scoring back-end…
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…
Speaker recognition with two-step multi-modal deep cleansing
Ruijie Tao, Kong Aik Lee, Zhan Shi +1
Neural network-based speaker recognition has achieved significant improvement in recent years. A robust speaker representation learns meaningful knowledge from both hard and easy s…
Self-Supervised Training of Speaker Encoder with Multi-Modal Diverse Positive Pairs
Ruijie Tao, Kong Aik Lee, Rohan Kumar Das +2
We study a novel neural architecture and its training strategies of speaker encoder for speaker recognition without using any identity labels. The speaker encoder is trained to ext…
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…
Self-supervised Speaker Recognition with Loss-gated Learning
Ruijie Tao, Kong Aik Lee, Rohan Kumar Das +2
In self-supervised learning for speaker recognition, pseudo labels are useful as the supervision signals. It is a known fact that a speaker recognition model doesn't always benefit…
ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection
Junichi Yamagishi, Xin Wang, Massimiliano Todisco +8
ASVspoof 2021 is the forth edition in the series of bi-annual challenges which aim to promote the study of spoofing and the design of countermeasures to protect automatic speaker v…
ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan
Héctor Delgado, Nicholas Evans, Tomi Kinnunen +8
The automatic speaker verification spoofing and countermeasures (ASVspoof) challenge series is a community-led initiative which aims to promote the consideration of spoofing and th…
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…
ASVspoof 2019: spoofing countermeasures for the detection of synthesized, converted and replayed speech
Andreas Nautsch, Xin Wang, Nicholas Evans +7
The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV). This paper describes the third in a series of bi-annual chall…
Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals
Tomi Kinnunen, Héctor Delgado, Nicholas Evans +8
Recent years have seen growing efforts to develop spoofing countermeasures (CMs) to protect automatic speaker verification (ASV) systems from being deceived by manipulated or artif…
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…
Extrapolating false alarm rates in automatic speaker verification
Alexey Sholokhov, Tomi Kinnunen, Ville Vestman +1
Automatic speaker verification (ASV) vendors and corpus providers would both benefit from tools to reliably extrapolate performance metrics for large speaker populations without co…
Neural i-vectors
Ville Vestman, Kong Aik Lee, Tomi H. Kinnunen
Deep speaker embeddings have been demonstrated to outperform their generative counterparts, i-vectors, in recent speaker verification evaluations. To combine the benefits of high p…
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…
Short-duration Speaker Verification (SdSV) Challenge 2021: the Challenge Evaluation Plan
Hossein Zeinali, Kong Aik Lee, Jahangir Alam +1
This document describes the Short-duration Speaker Verification (SdSV) Challenge 2021. The main goal of the challenge is to evaluate new technologies for text-dependent (TD) and te…
Voice Biometrics Security: Extrapolating False Alarm Rate via Hierarchical Bayesian Modeling of Speaker Verification Scores
Alexey Sholokhov, Tomi Kinnunen, Ville Vestman +1
How secure automatic speaker verification (ASV) technology is? More concretely, given a specific target speaker, how likely is it to find another person who gets falsely accepted a…
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Xin Wang, Junichi Yamagishi, Massimiliano Todisco +37
Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is v…
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…
ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection
Massimiliano Todisco, Xin Wang, Ville Vestman +7
ASVspoof, now in its third edition, is a series of community-led challenges which promote the development of countermeasures to protect automatic speaker verification (ASV) from th…