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 76–100 of 435 papers

TitleStatusHype
Bayesian calibration for forensic evidence reporting—0
3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Speaker Verification and Diarization—0
Disentangled representation learning for multilingual speaker recognition—0
A Generative Model for Score Normalization in Speaker Recognition—0
Avengers Assemble: Amalgamation of Non-Semantic Features for Depression Detection—0
Discriminatively Re-trained i-vector Extractor for Speaker Recognition—0
Disentangled Speaker Representation Learning via Mutual Information Minimization—0
Disentangled Speech Representation Learning Based on Factorized Hierarchical Variational Autoencoder with Self-Supervised Objective—0
Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation—0
Deep learning methods in speaker recognition: a review—0
A new Speech Feature Fusion method with cross gate parallel CNN for Speaker Recognition—0
Adversarial Speaker Verification—0
Deep factorization for speech signal—0
Deep CNN based feature extractor for text-prompted speaker recognition—0
A Unified Deep Neural Network for Speaker and Language Recognition—0
Automatic Speech Recognition on a Firefighter TETRA Broadcast Channel—0
Deep Learning for Single and Multi-Session i-Vector Speaker Recognition—0
Attention and DCT based Global Context Modeling for Text-independent Speaker Recognition—0
DeepMSRF: A novel Deep Multimodal Speaker Recognition framework with Feature selection—0
Deep neural network based i-vector mapping for speaker verification using short utterances—0
Deep Neural Networks for Automatic Speaker Recognition Do Not Learn Supra-Segmental Temporal Features—0
An Exploration of ECAPA-TDNN and x-vector Speaker Representations in Zero-shot Multi-speaker TTS—0
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends—0
An improved uncertainty propagation method for robust i-vector based speaker recognition—0
Data augmentation versus noise compensation for x- vector speaker recognition systems in noisy environments—0
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Benchmark Results

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