SOTAVerified

Speaker Recognition

Speaker Recognition is the process of identifying or confirming the identity of a person given his speech segments.

Source: Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–400 of 435 papers

TitleStatusHype
Study of Pre-processing Defenses against Adversarial Attacks on State-of-the-art Speaker Recognition Systems—0
3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Speaker Verification and Diarization—0
SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning—0
SUT Submission for NIST 2016 Speaker Recognition Evaluation: Description and Analysis—0
Synth2Aug: Cross-domain speaker recognition with TTS synthesized speech—0
Synthetic Speech Source Tracing using Metric Learning—0
System Combination for Short Utterance Speaker Recognition—0
Tackling the Score Shift in Cross-Lingual Speaker Verification by Exploiting Language Information—0
Team HYU ASML ROBOVOX SP Cup 2024 System Description—0
Text-To-Speech Synthesis In The Wild—0
The 2021 NIST Speaker Recognition Evaluation—0
Thech. Report: Genuinization of Speech waveform PMF for speaker detection spoofing and countermeasures—0
The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA—0
The CORAL++ Algorithm for Unsupervised Domain Adaptation of Speaker Recogntion—0
The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022—0
The DKU-DukeECE-Lenovo System for the Diarization Task of the 2021 VoxCeleb Speaker Recognition Challenge—0
The DKU-DukeECE System for the Self-Supervision Speaker Verification Task of the 2021 VoxCeleb Speaker Recognition Challenge—0
The DKU-MSXF Diarization System for the VoxCeleb Speaker Recognition Challenge 2023—0
The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 2023—0
The DKU Replay Detection System for the ASVspoof 2019 Challenge: On Data Augmentation, Feature Representation, Classification, and Fusion—0
The ETAPE speech processing evaluation—0
SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems—0
The Graph feature fusion technique for speaker recognition based on wav2vec2.0 framework—0
The HUAWEI Speaker Diarisation System for the VoxCeleb Speaker Diarisation Challenge—0
The IBM 2016 Speaker Recognition System—0
The IBM Speaker Recognition System: Recent Advances and Error Analysis—0
The IDLAB VoxCeleb Speaker Recognition Challenge 2021 System Description—0
The ID R&D VoxCeleb Speaker Recognition Challenge 2023 System Description—0
The Intelligent Voice 2016 Speaker Recognition System—0
The JHU submission to VoxSRC-21: Track 3—0
The Kriston AI System for the VoxCeleb Speaker Recognition Challenge 2022—0
The Newsbridge -Telecom SudParis VoxCeleb Speaker Recognition Challenge 2022 System Description—0
The NIST CTS Speaker Recognition Challenge—0
The OCON model: an old but green solution for distributable supervised classification for acoustic monitoring in smart cities—0
The Phonexia VoxCeleb Speaker Recognition Challenge 2021 System Description—0
The Reasonable Effectiveness of Speaker Embeddings for Violence Detection—0
The ReturnZero System for VoxCeleb Speaker Recognition Challenge 2022—0
The Royalflush System for VoxCeleb Speaker Recognition Challenge 2022—0
The SpeakIn System Description for CNSRC2022—0
The UPC Speaker Verification System Submitted to VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20)—0
The VoxCeleb Speaker Recognition Challenge: A Retrospective—0
Prosody-Driven Privacy-Preserving Dementia DetectionCode0
COVID-19 Patient Detection from Telephone Quality Speech DataCode0
Deep generative LDACode0
Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networksCode0
Curricular SincNet: Towards Robust Deep Speaker Recognition by Emphasizing Hard Samples in Latent SpaceCode0
To train or not to train adversarially: A study of bias mitigation strategies for speaker recognitionCode0
Private kNN-VC: Interpretable Anonymization of Converted SpeechCode0
Baselines and Protocols for Household Speaker RecognitionCode0
Risk of re-identification for shared clinical speech recordingsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1w2v2-aamEER1.88—Unverified
2WavLM+ECAPA-TDNNEER0.39—Unverified