Speaker Verification
170 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
Additive Margin in Contrastive Self-Supervised Frameworks to Learn Discriminative Speaker Representations
Implementing these two modifications to SimCLR improves performance and results in 7. 85% EER on VoxCeleb1-O, outperforming other equivalent methods.
Text-dependent Speaker Verification (TdSV) Challenge 2024: Challenge Evaluation Plan
This document outlines the Text-dependent Speaker Verification (TdSV) Challenge 2024, which centers on analyzing and exploring novel approaches for text-dependent speaker verification.
Zero-Shot Multi-Lingual Speaker Verification in Clinical Trials
This represents a significant step in developing more versatile and efficient speaker verification systems for cognitive and mental health clinical trials that can be used across a wide range of languages and dialects, substantially reducing the effort required to develop speaker verification systems for multiple languages.
Asymmetric and trial-dependent modeling: the contribution of LIA to SdSV Challenge Task 2
The SdSv challenge Task 2 provided an opportunity to assess efficiency and robustness of modern text-independent speaker verification systems.
KunquDB: An Attempt for Speaker Verification in the Chinese Opera Scenario
This work aims to promote Chinese opera research in both musical and speech domains, with a primary focus on overcoming the data limitations.
Efficient Adapter Tuning of Pre-trained Speech Models for Automatic Speaker Verification
With excellent generalization ability, self-supervised speech models have shown impressive performance on various downstream speech tasks in the pre-training and fine-tuning paradigm.
Probing the Information Encoded in Neural-based Acoustic Models of Automatic Speech Recognition Systems
Following many researches in neural networks interpretability, we propose in this article a protocol that aims to determine which and where information is located in an ASR acoustic model (AM).
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.
