Speaker Verification
12개 벤치마크 · 논문 805편 · 이 태스크의 논문 보기 →
Benchmarks
VoxCeleb
VoxCeleb1
CALLHOME
CN-CELEB
ASVspoof 2019 - LA
VoxCeleb2
Most implemented
Generalized End-to-End Loss for Speaker Verification
Speaker Recognition from Raw Waveform with SincNet
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning
RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification
Text-Independent Speaker Verification Using 3D Convolutional Neural Networks
Papers
NARU: A Benchmark for NARrative Evolution and Cultural Nuance Understanding in Japanese Extreme Long Video
Long-form video understanding encompasses tasks that go beyond retrieving isolated events, including tracking an evolving narrative and interpreting social meaning that may remain implicit. However, existing benchmarks r…
Speaker VerificationMultimodal Speaker Verification as a Threat to Speaker Anonymization
Most automatic speaker verification (ASV) systems operate on individual utterances, despite real-world interactions typically consisting of multiple utterances. As speech accumulates, increasingly rich speaker informatio…
Speaker VerificationLarge Audio Language Models for Spoofing-Aware Speaker Verification
Recent advances in text-to-speech and voice cloning make high-quality spoofing inexpensive and scalable, threatening voice authentication systems, especially automatic speaker verification (ASV). Existing defenses mainly…
Speaker VerificationDeepFake DetectionNouveauVoice: Generating Novel Pseudo Speakers for Voice Anonymization
Advanced neural technologies in speech synthesis and voice conversion (VC) have introduced severe risks to personal privacy, necessitating robust Speaker Anonymization Systems (SAS). Existing SAS approaches modify voice …
Speaker VerificationVoice ConversionSpeech SynthesisDisentangling Speaker and Language Effects in Cross-Lingual Speaker Verification for Iberian Languages
Cross-lingual speaker verification (SV) systems typically exhibit performance degradation when enrollment and test utterances are spoken in different languages. However, standard evaluation protocols confound language mi…
Cross-Lingual TransferSpeaker VerificationSparsity-Inducing Divergence Losses for Biometric Verification
Performance in face and speaker verification is largely driven by margin-penalty softmax losses such as CosFace and ArcFace. Recently introduced $α$-divergence loss functions offer a compelling alternative, particularly …
Speaker VerificationFace Verification