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Start Over Author "Nuance Communications [Spain]" Topic [info.info-lg]computer science [cs]/machine learning [cs.lg]
3 results on '"Nuance Communications [Spain]"'

1. Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion

Author

Hye-jin Shim, Hemlata Tak, Xuechen Liu, Hee-Soo Heo, Jee-weon Jung, Joon Son Chung, Soo-Whan Chung, Ha-Jin Yu, Bong-Jin Lee, Massimiliano Todisco, Héctor Delgado, Kong Aik Lee, Md Sahidullah, Tomi Kinnunen, Nicholas Evans, Seoul National University [Seoul] (SNU), Eurecom [Sophia Antipolis], University of Eastern Finland, Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Naver Corporation, Department of Electrical Engineering [Korea Advanced Institute of Science and Technology] (KAIST), Korea Advanced Institute of Science and Technology (KAIST), Nuance Communications [Spain], Agency for science, technology and research [Singapore] (A*STAR), and ANR-19-CE23-0001,ExTENSoR,Réseaux de neurones évolutifs end-to-end pour la reconnaissance du locuteur(2019)

Subjects
FOS: Computer and information sciences, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Sound (cs.SD), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Computer Science - Sound, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent, they have typically evolved as standalone sub-systems whereby CM solutions are usually designed for a fixed ASV system. The work reported in this paper aims to gauge the improvements in reliability that can be gained from their closer integration. Results derived using the popular ASVspoof2019 dataset indicate that the equal error rate (EER) of a state-of-the-art ASV system degrades from 1.63% to 23.83% when the evaluation protocol is extended with spoofed trials.%subjected to spoofing attacks. However, even the straightforward integration of ASV and CM systems in the form of score-sum and deep neural network-based fusion strategies reduce the EER to 1.71% and 6.37%, respectively. The new Spoofing-Aware Speaker Verification (SASV) challenge has been formed to encourage greater attention to the integration of ASV and CM systems as well as to provide a means to benchmark different solutions., 8 pages, accepted by Odyssey 2022

Published
2022

2. ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection

Author

Massimiliano Todisco, Nicholas Evans, Junichi Yamagishi, Andreas Nautsch, Kong Aik Lee, Jose Patino, Xuechen Liu, Tomi Kinnunen, Héctor Delgado, Sahidullah, Xin Wang, National Institute of Informatics (NII), Eurecom [Sophia Antipolis], Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), University of Eastern Finland, Institute for Infocomm Research - I²R [Singapore], Nuance Communications [Spain], ASVspoof consortium, Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Machine Learning, Spoofing attack, Computer Science - Cryptography and Security, Computer science, Field (computer science), Computer Science - Sound, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Task (project management), Machine Learning (cs.LG), [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Human–computer interaction, Logical conjunction, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, [SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Voice activity detection, [INFO.INFO-MM]Computer Science [cs]/Multimedia [cs.MM], [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Variable (computer science), [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD], Physical access, Cryptography and Security (cs.CR), Communication channel, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

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 verification systems from manipulation. In addition to a continued focus upon logical and physical access tasks in which there are a number of advances compared to previous editions, ASVspoof 2021 introduces a new task involving deepfake speech detection. This paper describes all three tasks, the new databases for each of them, the evaluation metrics, four challenge baselines, the evaluation platform and a summary of challenge results. Despite the introduction of channel and compression variability which compound the difficulty, results for the logical access and deepfake tasks are close to those from previous ASVspoof editions. Results for the physical access task show the difficulty in detecting attacks in real, variable physical spaces. With ASVspoof 2021 being the first edition for which participants were not provided with any matched training or development data and with this reflecting real conditions in which the nature of spoofed and deepfake speech can never be predicated with confidence, the results are extremely encouraging and demonstrate the substantial progress made in the field in recent years., Accepted to the ASVspoof 2021 Workshop

Published
2021

3. Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing

Author

Xin Wang, Kong Aik Lee, Massimiliano Todisco, Junichi Yamagishi, Andreas Nautsch, Md. Sahidullah, Héctor Delgado, Tomi Kinnunen, Nicholas Evans, University of Eastern Finland, Eurecom [Sophia Antipolis], Speech Modeling for Facilitating Oral-Based Communication (MULTISPEECH), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), National Institute of Informatics (NII), Nuance Communications [Spain], Institute for Infocomm Research (I2R), This work was supported by a number of projects and funding sources: VoicePersonae, supported by the French Agence Nationale de la Recherche (ANR) and the Japan Science and Technology Agency (JST) with grant No. JPMJCR18A6, Academy of Finland (proj. 309629), Region Grand Est, France., Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Sound (cs.SD), Computer science, multi-dimensional scaling, 02 engineering and technology, Classifier, Statistics - Applications, Computer Science - Sound, Machine Learning (cs.LG), Set (abstract data type), 030507 speech-language pathology & audiology, 03 medical and health sciences, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Audio and Speech Processing (eess.AS), Classifier (linguistics), 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, Applications (stat.AP), Representation (mathematics), [STAT.AP]Statistics [stat]/Applications [stat.AP], Receiver operating characteristic, business.industry, Visual comparison, 020206 networking & telecommunications, Pattern recognition, Mixture model, Automatic summarization, Adjacency list, Artificial intelligence, 0305 other medical science, business, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Whether it be for results summarization, or the analysis of classifier fusion, some means to compare different classifiers can often provide illuminating insight into their behaviour, (dis)similarity or complementarity. We propose a simple method to derive 2D representation from detection scores produced by an arbitrary set of binary classifiers in response to a common dataset. Based upon rank correlations, our method facilitates a visual comparison of classifiers with arbitrary scores and with close relation to receiver operating characteristic (ROC) and detection error trade-off (DET) analyses. While the approach is fully versatile and can be applied to any detection task, we demonstrate the method using scores produced by automatic speaker verification and voice anti-spoofing systems. The former are produced by a Gaussian mixture model system trained with VoxCeleb data whereas the latter stem from submissions to the ASVspoof 2019 challenge., Comment: Accepted to Interspeech 2021. Example code available at https://github.com/asvspoof-challenge/classifier-adjacency

Published
2021
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