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 101–125 of 435 papers

TitleStatusHype
A Reinforcement Learning Framework for Online Speaker Diarization—0
Can We Use Speaker Recognition Technology to Attack Itself? Enhancing Mimicry Attacks Using Automatic Target Speaker Selection—0
Dynamic Recognition of Speakers for Consent Management by Contrastive Embedding Replay—0
Arabic Speech Rhythm Corpus: Read and Spontaneous Speaking Styles—0
A Machine of Few Words -- Interactive Speaker Recognition with Reinforcement Learning—0
Call My Net 2: A New Resource for Speaker Recognition—0
BUT VOiCES 2019 System Description—0
基於數字文本相關之語者驗證系統的研究與實作 (Study and Implementation on Digit-related Speaker Verification) [In Chinese]—0
Disentangled Speaker Representation Learning via Mutual Information Minimization—0
BUT System Description to VoxCeleb Speaker Recognition Challenge 2019—0
Disentangled representation learning for multilingual speaker recognition—0
Discriminatively Re-trained i-vector Extractor for Speaker Recognition—0
Building and Evaluation of a Real Room Impulse Response Dataset—0
Disentangled Speech Embeddings using Cross-modal Self-supervision—0
Disentangled Speech Representation Learning Based on Factorized Hierarchical Variational Autoencoder with Self-Supervised Objective—0
Disentangling Voice and Content with Self-Supervision for Speaker Recognition—0
Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation—0
Dr-Vectors: Decision Residual Networks and an Improved Loss for Speaker Recognition—0
A Novel Speech Feature Fusion Algorithm for Text-Independent Speaker Recognition—0
A Lightweight Speaker Recognition System Using Timbre Properties—0
Embeddings for DNN speaker adaptive training—0
End-to-end Recurrent Denoising Autoencoder Embeddings for Speaker Identification—0
End-to-end training of time domain audio separation and recognition—0
Enhancing Open-Set Speaker Identification through Rapid Tuning with Speaker Reciprocal Points and Negative Sample—0
Differences in Speaker Individualising Information between Case Particles and Fillers in Spoken Japanese—0
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Benchmark Results

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