Speaker Verification

Follow this topic Ask about this topic

Momentum

7 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 40

All topics

On Low-Bit Quantization Errors in Speaker Verification: Diagnostic and Mitigation
Jun 6, 2026Hugo Leguillier, Driss Matrouf, Guillaume Lechien +1Mixed-Precision Quantization Adaptive Inference
Avignon University, LIA, UPR 4128, France · Aday, France
SpeakerCard-1M: An Evidence-Grounded Corpus for In-the-Wild Speaker Verification
Jun 2, 2026Junyi Peng, Oldřich Plchot, Xiao Song +9Audio-Language Model Evaluation Speaker Verification
Brno University of Technology, Czechia · Peking University, China · Xiaomi, China +3
A Training-Efficient Transformer-Based Anti-Spoofing Network for Logical Access in ASVspoof 5
Jun 2, 2026Sidan Yin, Bo ZhaoAudio Deepfake Detection Speaker Verification
San Domenico School, San Anselmo, CA, United States · University of Washington, Seattle, WA, United States
SpeakerLLM: A Speaker-Specialized Audio-LLM for Speaker Understanding and Verification Reasoning
May 14, 2026KiHyun Nam, Jungwoo Heo, Siu Bae +2Audio Representation Learning Speaker Verification
Korea Advanced Institute of Science and Technology (KAIST) · University of Seoul
Text-Dependent Speaker Verification (TdSV) Challenge 2024: Team Naive System Report
May 14, 2026Amir Mohammad Rostami, Pourya JafarzadehSpeaker Verification
Self-Organized and Independent Participants
LASE: Language-Adversarial Speaker Encoding for Indic Cross-Script Identity Preservation
May 1, 2026Venkata Pushpak Teja MentaAdversarial Training Adversarial Representation Learning
Praxel Ventures
Enhancing Speaker Verification with Whispered Speech via Post-Processing
Apr 22, 2026Magdalena Gołębiowska, Piotr SygaAudio Representation Learning Speaker Verification
Department of Artificial Intelligence, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, Wroclaw, 50-370, Poland
Quantifying Cross-Lingual Transfer in Paralinguistic Speech Tasks
Mar 9, 2026Pol Buitrago, Oriol Pareras, Federico Costa +1Speaker Verification Speech Processing
Barcelona Supercomputing Center (BSC), Spain · Universitat Polit`ecnica de Catalunya (UPC), Spain
CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking
Mar 2, 2025Yiming Li, Kaiying Yan, Jiawen Diao +4Speaker Verification Dataset Watermarking
College of Computing and Data Science, Nanyang Technological University, Singapore, 639798, Singapore · School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, 200030, China · Beijing Electronic Science and Technology Institute, Beijing, 100070, China +4
Spectral Masking and Interpolation Attack (SMIA): A Black-box Adversarial Attack against Voice Authentication and Anti-Spoofing Systems
Date pendingKamel Kamel, Hridoy Sankar Dutta, Keshav Sood +1Audio Deepfake Detection Speaker Verification
School of Information Technology, Deakin University, Australia

Jun 6, 2026, cs.SDJ/K move · Enter open · S save

On Low-Bit Quantization Errors in Speaker Verification: Diagnostic and Mitigation

Hugo Leguillier, Driss Matrouf, Guillaume Lechien, Mickael Rouvier

Avignon University, LIA, UPR 4128, France · Aday, France

Mixed-Precision Quantization Adaptive Inference Speaker Verification Quantization-Aware Training Low-Bit Quantization

Open paper AskPDF arXiv

Although low-bit quantization provides practical means to deploy speaker verification on resource-constrained devices, its effects on speaker verification performance remain poorly understood. In this paper, we study uniform K-means quantization-aware training of ResNet-36 and ResNet-200 through joint layer-wise and score-level analyses. Our layer-wise analysis highlights fragile components and shows that score degradation is not fully explained by weight distortion alone. We identify a clear knee point at 2 bits, with larger score drift and harmful decision flips concentrated near the FP32 threshold. Our score-level analysis reveals where and how score errors emerge under extreme quantization. Building on these findings, we propose a calibrated multi-precision cascade that resolves most trials at 2 bits and escalates only ambiguous cases, achieving performance close to FP32 while preserving the efficiency benefits of low-bit inference with substantially lower compute and memory costs.

Momentum

Related topics

Latest papers 40