November 21st, 2023, Hitotsubashi Hall 101-103
National Center of Sciences building, Hitotsubashi, Chiyoda-ku, Tokyo, JapanThe VoicePersonae Workshop– VoicePersonae overview and highlights –10:00-11:00 AM: Opening - Overview of the VoicePersonae Project
Speaker: Prof. Junichi Yamagishi (National Institute of Informatics,
Japan)

11:00-11:15 AM: Coffee break (15 min) @ Room 101-103
11:15-11:50 AM: Voice privacy Processing
Speaker: Prof. Jean-Francois Bonastre (Inria & Avignon University,
France)12:00-12:30 PM: Voice Biometrics & DeepFake Detection
Speaker: Prof. Nicholas Evans (EURECOM, France)

12:30-2:00 PM: Lunch BreakLunch map

– Theme 1: Speech generative models and related topics–
2:00-2:30 PM: From DSP and DNN to DNN/DSP: neural speech waveform models and its
applications in speech and music audio waveform modelling
Speaker: Dr. Xin Wang (NII, Japan)
Abstract: The waveform generation model is indispensable to speech
and music audio generation. The VoicePersonae project started when
the deep neural network (DNN) was introduced to produce thewaveform, an approach deviating from traditional digital signal
processing(DSP)
approaches.
Combining
DNN
and
DSP,
the
VoicePersonae project produced pioneering DSP plus DNN models,
such
as
the
neuralsource-filter
waveform
model
and
the
GAN-excited linear prediction waveform model. This presentation
will explain those outcomes, highlight the technical novelties,
and summarise models from the research community that follow the
DSP plus DNN (a.k.a differential DSP) approach.
References[1] Wang, Xin, Shinji Takaki, and Junichi Yamagishi. 2019. “Neural
Source-Filter-Based Waveform Model for Statistical Parametric
Speech Synthesis.” In Proc. ICASSP, 5916–5920.
[2] Wang, Xin, and Junichi Yamagishi. 2019. “Neural
Harmonic-plus-Noise Waveform Model with Trainable Maximum VoiceFrequency for Text-to-Speech Synthesis.” In Proc. SSW, 1–6.
[3] Wang, Xin, Shinji Takaki, and Junichi Yamagishi. 2020. “Neural
Source-Filter Waveform Models for Statistical Parametric Speech
Synthesis.” IEEE/ACM Transactions on Audio, Speech, and Language
Processing 28: 402–415.
[4] Juvela, Lauri, Bajibabu Bollepalli, Junichi Yamagishi, andPaavo Alku. 2019. “GELP: GAN-Excited Linear Prediction for Speech
Synthesis from Mel-Spectrogram.” In Proc. Interspeech, 694–699.
[5] Wang, Xin, and Junichi Yamagishi. 2020. “Using Cyclic Noise as
the Source Signal for Neural Source-Filter-Based Speech Waveform
Model.” In Proc. Interspeech, 1992–1996.
[6] Zhao, Yi, Xin Wang, Lauri Juvela, and Junichi Yamagishi. 2020.
“Transferring Neural Speech Waveform Synthesizers to MusicalInstrument Sounds Generation.” In Proc. ICASSP, 6269–6273.

2:30-3:00 PM: End-to-End Speech Synthesis and Its Entertainment Applications: Rakugo
Modelling & Musical Instrument Sound Modelling
Speaker: Dr. Shuhei Kato (Revcomm, Japan) & Dr. Yusuke Yasuda(Nagoya University, Japan)
Abstract: Text-to-speech synthesis is a technique for converting
text into speech. Recent developments in deep learning have led tothe emergence of end-to-end speech synthesis, a technique thatconverts text directly into speech. This method enables thegeneration of synthesised speech that is indistinguishable from

human speech. This method is also highly versatile and can handle
not only text-to-speech conversion, but also score-to-music
conversion within the same framework. However, from theperspective of entertaining people, it is extremely difficult todevelop synthesis technology that extends to professional humanperformers and musicians. In this project, we have improved
end-to-end speech synthesis to realise Rakugo speech synthesis andguitar music synthesis. In this presentation, we will present theresults of our research to realise synthesis technology toentertain people.

3:00-3:30 PM: From Human Ears to Deep Neural Networks: Automatic Evaluation of
Synthetic Speech and Audio Data
Speaker: Dr. Erica Cooper (National Institute of Informatics, Japan)& Mr. Wen-Ching Huang (Nagoya University, Japan)
Abstract: Evaluation of experimental speech synthesis systems islargely conducted by human listening tests, such as the
widely-used mean opinion score (MOS) test. These tests can betime-consuming and costly, precluding rapid experimental
iteration; however, existing objective metrics fall short ofcorrelating well with human judgments or generalizing to unseen
conditions. We will present the efforts of the VoicePersonae
project to address this problem, including the collection oflarge-scale public datasets and the development of strong baseline
MOS predictors by applying modern techniques such as
self-supervised learning based models for speech. We will alsopresent the experiences and lessons learned from two years of
running the VoiceMOS Challenge, an international shared task for
MOS prediction.
References[1] Lo, Chen-Chou, Szu-Wei Fu, Wen-Chin Huang, Xin Wang, Junichi
Yamagishi, Yu Tsao, and Hsin-Min Wang. 2019. “MOSNet: Deeplearning based objective assessment for voice conversion.” In
Proc. Interspeech, 1541-1545.
[2] Cooper, Erica, and Junichi Yamagishi. 2021. “How do Voicesfrom Past Speech Synthesis Challenges Compare Today?” In Proc.
11th ISCA Speech Synthesis Workshop, 183-188.
[3] Cooper, Erica, Wen-Chin Huang, Tomoki Toda, and Junichi
Yamagishi. 2022. “Generalization Ability of MOS Prediction
Networks.” In Proc. ICASSP, 8442-8446.
[4] Huang, Wen-Chin, Erica Cooper, Junichi Yamagishi, and Tomoki
Toda. 2022. “LDNet: Unified Listener Dependent Modeling in MOS
Prediction for Synthetic Speech.” In Proc. ICASSP, 896-900.
[5] Huang, Wen-Chin, Erica Cooper, Yu Tsao, Hsin-Min Wang, Tomoki
Toda, and Junichi Yamagishi. 2022. “The VoiceMOS Challenge 2022.”
In Proc. Interspeech, 4536-4540.
[6] Cooper, Erica, and Junichi Yamagishi. 2023. “Investigating
Range-Equalizing Bias in Mean Opinion Score Ratings of Synthesized
Speech.” In Proc. Interspeech, 1104-1108.

[7] Cooper, Erica, Wen-Chin Huang, Yu Tsao, Hsin-Min Wang, Tomoki
Toda, and Junichi Yamagishi. 2023. “The VoiceMOS Challenge 2023:
Zero-shot Subjective Speech Quality Prediction for Multiple
Domains.” Workshop on Automatic Speech Recognition andUnderstanding (to appear).
[8] Yadav, Hemant, Erica Cooper, Junichi Yamagishi, Sunayana
Sitaram, and Rajiv Ratn Shah. 2023. “Partial Rank Similarity
Minimization Method for Quality MOS Prediction of Unseen Speech
Synthesis Systems in Zero-Shot and Semi-supervised Setting.”
Workshop on Automatic Speech Recognition and Understanding (toappear).

3:30-3:45 PM: Coffee break (15 min) @ room 101-103– Theme 2: Speech security and deepfake detection–
3:45-4:15 PM: From Artefacts to Insights: A Topical Analysis of Voice Biometric Security
Speaker: Prof. Massimiliano Todisco (EURECOM, France) & Dr. Hemlata
Tak
Abstract: In recent years, voice biometric security has gained
significant attention for its potential to offer secure andconvenient authentication. However, the emergence of generative AI
or deepfake technology has raised concerns as it enables thecreation of highly realistic synthetic media, including speechrecordings. Speech deepfakes pose a serious threat to voicebiometric security systems, allowing unauthorized access andimpersonation. This presentation explores the interaction between
voice biometrics and security, especially in the face of thechallenges posed by deepfakes. To tackle these challenges, we
present advanced deepfake detection techniques that leverage graphneural networks (GNNs) and self-supervised learning based
front-ends. GNNs have demonstrated promising results in diverse
domains such as natural language processing and computer vision.
By exploiting the inherent graph structure of voice data, GNNs
effectively capture intricate relationships between acoustic
features and identify distinctive cues indicative of spoofing
attacks. This talk sheds light on the potential of self-supervised
learning and GNN-based solutions to enhance the robustness andreliability of voice biometric systems, ensuring the integrity andauthenticity of voice-based authentication processes.
In the last part of the talk, we will discuss Malafide, a universal
adversarial attack against voice biometrics. Through convolutional
noise and specialized filters, Malafide is able to compromise
spoofing countermeasures, posing significant threats even inblack-box settings.
References

[1] Evans, N. et al. Spoofing and countermeasures for automatic
speaker verification. in Proc. Interspeech 925–929, 2013.
[2] Sahidullah, M. et al. Introduction to voice presentation attack
detection and recent advances. in Handbook of BiometricAnti-Spoofing 321–361, 2019.
[3] Todisco, M. et al. “ASVspoof 2019: Future Horizons in Spoofed
and Fake Audio Detection.” Interspeech ,2019.
[4] Todisco, M. et al. “Constant Q cepstral coefficients: A
spoofing countermeasure for automatic speaker verification.”
Computer Speech Lang. 45, 516-535, 2017.
[5] Kamble, M. R. et al. Advances in anti-spoofing: from theperspective of ASVspoof challenges. APSIPA Trans. Signal Inf.
Process. 9, e2, 2020.
[6] Yamagishi, J. et al., “ASVspoof 2021: accelerating progress inspoofed and deepfake speech detection”, Proc. 2021 Edition of theAutomatic Speaker Verification and Spoofing Countermeasures
Challenge, 2021.
[7] Wu, Z. et al. "A comprehensive survey on graph neuralnetworks." IEEE transactions on neural networks and learning
systems, 32.1, 2020.
[8] Veličković, P. et al., "Graph Attention Networks”, in Proc.
International Conference on Learning Representations (ICLR), 2018.
[9] Mohamed, A. et al., “Self-Supervised Speech Representation
Learning: A Review”, IEEE Journal of Selected Topics in Signal
Processing 16 (6): 1179–1210. 2022.
[10] Jung, J. et al., “AASIST: Audio anti-spoofing using
integrated spectro-temporal graph attention networks”, in Proc.
ICASSP, 2022.
[11] Wang, X. et al., "Investigating self-supervised front ends
for speech spoofing countermeasures.", in Proc. Speaker Odyssey,2022.
[12] Tak, H. et al., “Automatic speaker verification spoofing anddeepfake detection using wav2vec 2.0 and data augmentation”, in
Proc. Speaker Odyssey, 2022.
[13] Liu, X. et al., “ASVspoof 2021: Towards Spoofed and Deepfake
Speech Detection in the Wild”, IEEE/ACM Transactions on Audio, Speech,
and Language Processing, 31, 2023.
[14] Panariello, M. et al. “Malafide: a novel adversarialconvolutive noise attack against deepfake and spoofing detection
systems.” in Proc. Interspeech, 2023.

4:15-4:45 PM: Partial Spoof Detection: Countermeasures for the Detection of Short Fake
Speech Segments Embedded in an Utterance
Speaker: Ms. Lin Zhang​​ (National Institute of Informatics, Japan)
Abstract: Automatic speaker verification is susceptible to various
manipulations and spoofing, such as text-to-speech synthesis,
voice conversion, replay, tampering, adversarial attacks, and soon. We consider a new spoofing scenario called Partial Spoof (PS)in which synthesized or transformed {speech} segments are embedded
into a bona fide utterance. While existing countermeasures (CMs)can detect fully spoofed utterances, there is a need for theiradaptation or extension to the PS scenario.

We propose various improvements to construct a significantly moreaccurate CM that can detect and locate short-generated spoofed{speech} segments at finer temporal resolutions. First, we
introduce newly developed self-supervised pre-trained models as
enhanced feature extractors. Second, we extend our PartialSpoof
databasebyaddingsegment
labelsforvarioustemporalresolutions. Since the short spoofed {speech} segments to beembedded by attackers are of variable length, six different
temporal resolutions are considered, ranging from as short as 20
ms to as large as 640 ms. Third, we propose a new CM that enables
the simultaneous use of the segment-level labels at different
temporal resolutions as well as utterance-level labels to execute
utterance- and segment-level detection at the same time. We also
show that the proposed CM is capable of detecting spoofing at theutterance level with low error rates in the PS scenario as well as
in a related logical access (LA) scenario. The equal error rates
of utterance-level detection on the PartialSpoof database andASVspoof 2019 LA database were 0.77% and 0.90%, respectively. ​4:45-5:15 PM: From Deepfake Detection and Segmentation to Restoration: Protection ofFacial Biometrics from Manipulative and Generative AI
Speaker: Dr. Huy H.Nguyen (National Institute of Informatics, Japan)& Dr. C.C. Chang (NII, Japan)
Abstract: With
the
rapid
advancementoftechnology, image
manipulation has evolved to a sophisticated level, enabling
effortless alteration of a subject’s identity, facial features,movements, and even the creation of entirely fabricated images orvideos, popularly known as deepfakes. The widespread use of social
media has facilitated the generation and dissemination of vast
amounts of data, encompassing personal information, news articles,
images, and videos. Unfortunately, this environment has also madeit easier for malicious actors to create and widely distributedeepfakes to large audiences. In response to this emerging threat,
we have pioneered comprehensive countermeasures against deepfakes,
focusing
ondetection,
segmentation,andrestoration.
Ourdetection methods assess the probability of a face being a
deepfake, while segmentation provides explainable information on
manipulated regions. Conversely, our innovative approach torestoration,termed“cybervaccination,”
involves
addingimperceptible but resilient latent noises to original images
before publication. These noises are crucial in restoring original
images from manipulated ones. Through extensive experimentation,
we have demonstrated the effectiveness of our proposed methods indeepfake detection, segmentation, and restoration.
References[1] Afchar, Darius, Vincent Nozick, Junichi Yamagishi, and Isao
Echizen. “MesoNet: a Compact Facial Video Forgery Detection
Network.” In 2018 IEEE International Workshop on Information
Forensics and Security (WIFS), pp. 1-7. IEEE, 2018.

[2] Nguyen, Huy H., T. Ngoc-Dung Tieu, Hoang-Quoc Nguyen-Son,Vincent Nozick, Junichi Yamagishi, and Isao Echizen. “Modular
Convolutional Neural Network for Discriminating between
Computer-Generated Images and Photographic Images.” In Proceedingsof the 13th international conference on availability, reliability
and security, pp. 1-10. 2018.
[3] Nguyen, Huy H., Junichi Yamagishi, and Isao Echizen.
“Capsule-Forensics: Using Capsule Networks to Detect Forged Images
and Videos.” In 2019 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), pp. 2307-2311. IEEE, 2019.
[4] Nguyen, Huy H., Junichi Yamagishi, and Isao Echizen.
“Capsule-Forensics Networks for Deepfake Detection.” In Handbook of
Digital Face Manipulation and Detection: From DeepFakes to Morphing
Attacks, pp. 275-301. Cham: Springer International Publishing,2022.
[5] Tolosana, Ruben, Christian Rathgeb, Ruben Vera-Rodriguez,
Christoph Busch, Luisa Verdoliva, Siwei Lyu, Huy H. Nguyen et al.
“Future Trends in Digital Face Manipulation and Detection.” In
Handbook of Digital Face Manipulation and Detection: From DeepFakes
to Morphing Attacks, pp. 463-482. Cham: Springer International
Publishing, 2022.
[6] Le, Trung-Nghia, Huy H. Nguyen, Junichi Yamagishi, and Isao
Echizen. “Robust Deepfake on Unrestricted Media: Generation and
Detection.” In Frontiers in Fake Media Generation and Detection,
pp. 81-107. Singapore: Springer Nature Singapore, 2022.
[7] Sun, YuYang, ZhiYong Zhang, Isao Echizen, Huy H. Nguyen,
ChangZhen Qiu, and Lu Sun. “Face Forgery Detection Based on Facial
Region Displacement Trajectory Series.” In Proceedings of theIEEE/CVF Winter Conference on Applications of Computer Vision, pp.
633-642. 2023.
[8] Nguyen, Huy H., Fuming Fang, Junichi Yamagishi, and Isao
Echizen. “Multi-Task Learning for Detecting and Segmenting
Manipulated Facial Images and Videos.” In 2019 IEEE 10th
International Conference on Biometrics Theory, Applications andSystems (BTAS), pp. 1-8. IEEE, 2019.
[9] Le, Trung-Nghia, Huy H. Nguyen, Junichi Yamagishi, and Isao
Echizen. “Openforensics: Large-Scale Challenging Dataset forMulti-Face Forgery Detection and Segmentation in-the-Wild.” In
Proceedings of the IEEE/CVF International Conference on Computer
Vision, pp. 10117-10127. 2021.
[10] Chang, Ching-Chun, Huy H. Nguyen, Junichi Yamagishi, and Isao
Echizen. “Cyber Vaccine for Deepfake Immunity.” IEEE Access (2023).
[11] Huang, Rong, Fuming Fang, Huy H. Nguyen, Junichi Yamagishi,
and Isao Echizen. “Security of Facial Forensics Models Against
Adversarial Attacks.” In 2020 IEEE International Conference on
Image Processing (ICIP), pp. 2236-2240. IEEE, 2020.

5:15-5:30 PM: Coffee break (15 min) @ room 101-103– Theme 3: Speech privacy and speaker anonymization –5:30-6:00 PM: Preserving Speaker’s Privacy and Utility: The VoicePrivacy Progress and
VoxCeleb2 Database anonymization
Speaker: Dr. Natalia Tomashenko (Avignon University, France) & Dr.
Xiaoxiao Miao (Singapore Institute of Technology, Singapore)
Abstract: Speech contains a lot of personal, private information(e.g., age, gender, personality traits, health and emotional
state, socio-economic status, geographical background, etc.) which
can be associated to the speaker’s identity using automatic
speaker recognition or metadata. Thus, collection, processing, andstorage of speech data poses serious privacy risks. Formed in
2020, the VoicePrivacy initiative is spearheading the effort todevelop privacy preservation solutions for speech technology.
In the first part, we provide an overview of privacy preservation
solutions for speech data, with a focus on voice anonymization. We
present the VoicePrivacy challenge design including the voice
anonymization task and evaluation metrics, and discuss findings ofthe first two challenge editions.
In the second part, we address the legal and ethical concerns that
led to the withdrawal of the VoxCeleb2 ASV dataset by creating a
privacy-friendly synthetic VoxCeleb2 dataset. Specifically, we
employ the state-of-the-art speaker anonymization techniques toanonymize authentic VoxCeleb2 dataset and evaluate the quality of
the generated speech in terms of privacy, utility, and fairness.
We also discuss the challenges of using synthetic data for thedownstream task of speaker verification.
References[1] The VoicePrivacy 2020 Challenge: Results and findings. Natalia
Tomashenko, Xin Wang, Emmanuel Vincent, Jose Patino, Brij Mohan Lal
Srivastava, Paul-Gauthier Noé, Andreas Nautsch, Nicholas Evans, Junichi
Yamagishi,
BenjaminO'Brien,Anaïs
Chanclu,
Jean-François
Bonastre,
Massimiliano Todisco, Mohamed Maouche.
Computer Speech and Language 2022
[2] The VoicePrivacy 2022 Challenge evaluation plan. Natalia Tomashenko, Xin
Wang, Xiaoxiao Miao, Hubert Nourtel, Pierre Champion, Massimiliano Todisco,
Emmanuel Vincent, Nicholas Evans, Junichi Yamagishi, Jean-François Bonastre[3] Speaker anonymisation using the McAdams coefficient. Jose Patino, Natalia
Tomashenko,
Massimiliano
Todisco,AndreasNautsch,NicholasEvans.
Interspeech 2021
[4] Privacy and utility of x-vector based speaker anonymization. Brij M. L.
Srivastava, Mohamed Maouche, Md Sahidullah, Emmanuel Vincent, Aurélien
Bellet, Marc Tommasi, Natalia Tomashenko, Xin Wang, Junichi Yamagishi.
IEEE/ACM Transactions on Audio, Speech, and Language Processing[5] Towards a unified assessment framework of speech pseudonymisation.
Paul-Gauthier
Noé,AndreasNautsch,NicholasEvans,Jose
Patino,Jean-François Bonastre, Natalia Tomashenko, Driss Matrouf. Computer Speech &Language, 2022.
[6] Speaker anonymization using orthogonal Householder neural network.
Xiaoxiao Miao, Xin Wang, Erica Cooper, Junichi Yamagishi, Natalia Tomashenko.
IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023.
[7] SynVox2: Towards a privacy-friendly VoxCeleb2 dataset. Xiaoxiao Miao, Xin
Wang, Erica Cooper, Junichi Yamagishi, Nicholas Evans, Massimiliano Todisco,Jean-François Bonastre, Mickael Rouvier, 2023.

6:00-6:10 PM: Closing
Speaker Bibliographies
Speaker portrait

Speaker info
Junichi Yamagishi (Senior member, IEEE) received a Ph.D. degree from the Tokyo
Institute of Technology (Tokyo Tech), Tokyo, Japan, in 2006. From 2007-2013, he wasa research fellow in the Centre for Speech Technology Research (CSTR) at the
University of Edinburgh, UK. He was appointed Associate Professor at the National
Institute of Informatics, Japan, in 2013. He is currently a Professor at NII, Japan. His
research topics include speech processing, machine learning, signal processing,biometrics, digital media cloning, and media forensics. He served previously as
co-organizer for the bi-annual ASVspoof Challenge and the bi-annual Voice
Conversion Challenge. He also served as a member of the IEEE Speech andLanguage Technical Committee (2013-2019), an Associate Editor of the IEEE/ACM
Transactions on Audio Speech and Language Processing (2014-2017), achairperson of ISCA SynSIG (2017-2021), and a Senior Area Editor of the IEEE/ACM
TASLP (2019-2023). He is currently a PI of JST-CREST and ANR supported
VoicePersonae project and a board member of IEEE Signal Processing Society
Education Board.
Nicholas Evans (Member, IEEE) is a Professor at EURECOM, France, where heheads research in Audio Security and Privacy. He received his Ph.D. from the
University of Wales Swansea, UK in 2004. He is a co-founder of the community led,
ASVspoof, SASV, and VoicePrivacy challenge series. He participated in the EU FP7
Tabula Rasa and EU H2020 OCTAVE projects, both involving antispoofing. Today, his
team is leading the EU H2020 TReSPAsS-ETN project, a training initiative in security
and privacy for multiple biometric characteristics. He co-edited the second and third
editions of the Handbook of Biometric Anti-Spoofing, is currently co-editing the
Handbook of Privacy and Security Matters in Biometric Technologies, served
previously on the IEEE Speech and Language Technical Committee and serves
currently as an associate editor for the IEEE Trans. on Biometrics, Behavior, andIdentity Science.
Jean-François Bonastre is Senior Researcher (Directeur de Recherche) at Inria
Defense & Security where he acts as Research head and a professor of computerscience at LIA, the computer science laboratory of Avignon University. He received
his PhD on automatic speaker recognition in 1994 and his “Habilitation à Diriger lesRecherches” (HDR) in 2000. He spent a sabbatical year at Panasonic SpeechTechnology Laboratory (Santa Barbara, California, USA) in 2002-2003. He was
vice-president of Avignon University from 2008 to 2015 and an auditor of the 26th
Mediterranean session of IHEDN (Institut des Hautes Etudes de la Défense
Nationale) in 2015. He was the director of the LIA and a member of the ScientificCommittee of the Montreal Computer Research Center (CRIM) from 2016 to 2020.
He was the President of the International Speech Communication Association (ISCA)
from 2011 to 2013 and the President of the Association Francophone de laCommunication Parlée from 2000 to 2004. He is one of the founders of ISCA's
Special Interest Group "Speaker and Language Characterization" (SPLC). He wasawarded "ISCA Fellow Member" in 2021, and “Fellow member of the Asia-Pacific
Artificial Intelligence Association” in 2023. He is also an IEEE Senior Member andwas elected member of the IEEE Speech and Language Technical Committee andIEEE Biometrics Council. Jean-François Bonastre is member of the scientificcommittee of the main speech conferences and is “Technical Program Chair” ofInterspeech 2024.

Xin Wang is a project associate professor at the National Institute of Informatics (NII),
Japan. He received the Ph.D. degree from SOKENDAI/NII, Japan, in 2018. Before
that, he received M.S. and B.E degrees from the University of Science andTechnology of China and University of Electronic Science and Technology of China in2015 and 2012, respectively. His research interests include statistical speechsynthesis, speech security, and machine learning. He is a co-organizer of the
ASVspoof (2019, 2021, ASVspoof5) and VoicePrivacy (2020, 2022) challenges. He isa JST PRESTO Researcher from 2023 October.

Shuhei Kato received B.E. and Master of Information Science and Technology
degrees from The University of Tokyo, Japan, in 2011 and 2013, respectively. He
received a Ph.D. degree from The Graduate University for Advanced Studies,
SOKENDAI, Japan, in 2021. Since 2020, he has worked as a research engineer for
RevComm Inc., Japan. His research interests include speech processing, especiallyspeech synthesis.

Yusuke Yasuda received the B.S. and M.S. degree at Waseda University, Japan, in2012 and 2014, respectively. He received the Ph.D degree at graduate university foradvanced studies, SOKENDAI, Japan, in 2021. He was a Research Assistant with the
National Institute of Informatics, Japan from 2018 to 2021. Since 2021, he has beenan Assistant Project Professor with the Information Technology Center, Nagoya
University. He received the 2nd Yoshida Award and the 20th Best Student Paper
Award from the Acoustical Society of Japan. His research interests include statisticalmachine learning and speech synthesis.

Erica Cooper received a B.Sc. degree and M.Eng. degree both in electricalengineering and computer science from the Massachusetts Institute of Technology,
Cambridge, MA, USA, in 2009 and 2010, respectively. She received a Ph.D. degree
in computer science from Columbia University, New York, NY, USA, in 2019. She iscurrently a Project Assistant Professor with the National Institute of Informatics,
Chiyoda, Tokyo, Japan. Her research interests include statistical machine learningand speech synthesis. Dr. Cooper's awards include the 3rd Prize in the CSAW Voice
Biometrics and Speech Synthesis Competition, the Computer Science Service Award
from Columbia University, and the Best Poster Award in the Speech Processing
Courses in Crete. She has served as a co-organizer for the VoiceMOS Challenge in
2022 and 2023.
Wen-Chin Huang received the B.S. degree from National Taiwan University, Taipei,
Taiwan, in 2018 and the M.S. degree from Nagoya University, Nagoya, Japan in
2021. He was a Research Assistant with the Institute of Information Science,
Academia Sinica, Taipei, Taiwan from 2017 to 2019. He is currently a Ph.D. candidate
at Nagoya University, Nagoya, Japan. He was the recipient of the Best Student Paper
Award in ISCSLP2018, the 16th IEEE Signal Processing Society Japan Student Best
Paper Award, the 2023 Outstanding Graduate Student Award of Nagoya University,and the research fellowship for young scientists (DC1) from the Japan Society for the
Promotion of Science in 2021. He was a co-organizer of the Voice Conversion
Challenge 2020, the Singing Voice Conversion Challenge 2023, and VoiceMOS
Challenge 2022 and 2023. His research focuses on deep learning applications tospeech processing, with a main focus in voice conversion and speech quality
assessment.

Massimiliano Todisco (Member, IEEE) is currently an Associate Professor (MCF) with
EURECOM, France. He received the Ph.D. degree in sensorial and learning systems
engineering from the University of Rome Tor Vergata, Italy, in 2012. He is currently
the principal investigator and coordinator for TReSPAsS-ETN, a H2020 Marie Curie
ITN, RESPECT, a French-German funded project and P-SPIKE a French funded
project. He co-organises the ASVspoof and VoicePrivacy challenge series. His
research delves into speech processing, voice biometrics, fake audio and speechdetection, and algorithms that bolster privacy. In 2020, he was the recipient of theInternational Speech Communication Association (ISCA) Award for the best article
published in the journal ‘Computer Speech and Language’ during the quinquennium
2015–2019 for his contribution to speaker verification anti-spoofing. He co-edit thefirst edition of the Handbook of Privacy and Security Matters in BiometricTechnologies.
Hemlata Tak received her Ph.D. degree from Sorbonne University, France. She
received her Master’s degree in 2018 from DA-IICT, Gandhinagar, India. She
co-organized the inaugural edition of the Spoof-Aware Speaker Verification (SASV)
Challenge 2022. She is also a co-organiser of the ASVspoof 5 Challenge. Her
research interests include voice biometrics, audio deepfake detection andanti-spoofing.

Lin Zhang (Student Member, IEEE) received an M.S. degree from Tianjin University,
China, in 2020. She served as a Research Assistant with the SMIIP Lab at Duke
Kunshan University in 2020 and Visitor with Speech@FIT at Brno University ofTechnology in 2023. She is currently working toward the Ph.D. degree with the
SOKENDAI/National Institute of Informatics, Japan. Her research interests includeanti-spoofing, speaker recognition, speaker diarization, and machine learning.

Huy H. Nguyen received the Ph.D. from the Graduate University for Advanced
Studies, SOKENDAI, Japan, in 2022. He is a Specially Appointed Assistant Professor
with the National Institute of Informatics, Tokyo, Japan. He received the Best Student
Award from the National Institute of Informatics, Japan (2020), the Telecom
Interdisciplinary Research Award from the Telecommunication Advancement
Foundation, Japan (2023), the Best Paper Award (WIFS 2017), Excellent Paper
Awards (three journal papers) from the Institute of Electronics, Information andCommunication Engineers (2023), Japan. He serves as a reviewer for several
conferences (NeurIPS, ICML, WACV, ICME, ACL RR, APSIPA ASC) and journals(IEEE [Access, TIP, TIFS], IEEE/CAA JAS, ACM TOMM, Elsevier [PRLETTERS,EAAI], EURASIP JIVP, IEICE). His research interests include security and privacy inbiometrics & machine learning, and disinformation.

Ching-Chun Chang received his PhD in Computer Science from the University of
Warwick, UK, in 2019. He participated in a short-term scientific mission supported by
European Cooperation in Science and Technology Actions at the Faculty of Computer
Science, Otto-von-Guericke-Universität Magdeburg, Germany, in 2016. He was
granted the Marie-Curie fellowship and participated in a research and innovation staff
exchange scheme supported by Marie Skłodowska-Curie Actions at the Department
of Electrical and Computer Engineering, New Jersey Institute of Technology, USA, in
2017. He was a Visiting Scholar with the School of Computer and Mathematics,
Charles Sturt University, Australia, in 2018, and with the School of Information
Technology, Deakin University, Australia, in 2019. He was a Research Fellow with the
Department of Electronic Engineering, Tsinghua University, China, in 2020. He iscurrently a Postdoctoral Fellow with the National Institute of Informatics, Japan. His
research interests include steganography, forensics, biometrics, cybersecurity,
computer vision, computational linguistics and artificial intelligence.
Natalia Tomashenko is a researcher at the University of Avignon, France. She
received the Ph.D. degree in computer science from the University of Le Mans,
France. Her research interests focus on statistical machine learning for speech andlanguage processing with application to automatic speech and speaker recognition,spoken language understanding, machine translation, and speech privacy. She has
been a lead co-organizer of the 1st and 2nd VoicePrivacy Challenges, guest editor for
the Computer Speech & Language journal, co-chair of the 2nd Symposium on
Security and Privacy in Speech Communication, and organiser of other scientificevents.
Xiaoxiao Miao is a postdoctoral researcher at the National Institute of Informatics(NII), Japan until October 2023. She will be an assistant professor at the Singapore
Institute of Technology (SIT) in November 2023. She received the Ph.D. degree fromthe Institute of Acoustics, Chinese Academy of Sciences/University Chinese
Academy of Sciences, in 2021. Her research interests include speaker and language
recognition, speech security, and machine learning. She is a co-organizer of the latest
VoicePrivacy challenge.