Speaker Verification
171 papers with code • 5 benchmarks • 6 datasets
Speaker verification is the verifying the identity of a person from characteristics of the voice.
( Image credit: Contrastive-Predictive-Coding-PyTorch )
Libraries
Use these libraries to find Speaker Verification models and implementations
VoxCeleb1
VoxCeleb2
VGG-Sound
CN-CELEB
EVI
Latest papers with no code
Probing Self-supervised Learning Models with Target Speech Extraction
TSE uniquely requires both speaker identification and speech separation, distinguishing it from other tasks in the Speech processing Universal PERformance Benchmark (SUPERB) evaluation.
LightCAM: A Fast and Light Implementation of Context-Aware Masking based D-TDNN for Speaker Verification
Traditional Time Delay Neural Networks (TDNN) have achieved state-of-the-art performance at the cost of high computational complexity and slower inference speed, making them difficult to implement in an industrial environment.
Adversarial Data Augmentation for Robust Speaker Verification
This adversarial learning empowers the network to generate speaker embeddings that can deceive the augmentation classifier, making the learned speaker embeddings more robust in the face of augmentation variations.
Enhancement of a Text-Independent Speaker Verification System by using Feature Combination and Parallel-Structure Classifiers
In this work, we propose the combination of two SVM-based classifiers with different kernel functions: Linear kernel and Gaussian Radial Basis Function (RBF) kernel with a Logistic Regression (LR) classifier.
Adversarial speech for voice privacy protection from Personalized Speech generation
For validation, we employ the open-source pre-trained YourTTS model for speech generation and protect the target speaker's speech in the white-box scenario.
Empowering Communication: Speech Technology for Indian and Western Accents through AI-powered Speech Synthesis
The architecture of the system comprises a speaker verification system, a synthesizer, a vocoder, and noise reduction.
Generalizing Speaker Verification for Spoof Awareness in the Embedding Space
To this end, we propose to generalize the standalone ASV (G-SASV) against spoofing attacks, where we leverage limited training data from CM to enhance a simple backend in the embedding space, without the involvement of a separate CM module during the test (authentication) phase.
ECAPA2: A Hybrid Neural Network Architecture and Training Strategy for Robust Speaker Embeddings
In this paper, we present ECAPA2, a novel hybrid neural network architecture and training strategy to produce robust speaker embeddings.
Exploratory Evaluation of Speech Content Masking
Most recent speech privacy efforts have focused on anonymizing acoustic speaker attributes but there has not been as much research into protecting information from speech content.
VOT: Revolutionizing Speaker Verification with Memory and Attention Mechanisms
Speaker verification is to judge the similarity of two unknown voices in an open set, where the ideal speaker embedding should be able to condense discriminant information into a compact utterance-level representation that has small intra-speaker distances and large inter-speaker distances. We propose a novel model named Voice Transformer(VOT) for speaker verification.
