Improving Noise Robustness in Self-supervised Pre-trained Model for Speaker Verification

Chan-yeong Lim; Hyun-seo Shin; Ju-ho Kim; Jungwoo Heo; Kyo-Won Koo; Seung-bin Kim; Ha-Jin Yu

doi:10.21437/interspeech.2024-1630

ScienceGate Book Chapters

JOURNAL ARTICLE

Improving Noise Robustness in Self-supervised Pre-trained Model for Speaker Verification

Chan-yeong Lim Hyun-seo Shin Ju-ho Kim Jungwoo Heo Kyo-Won Koo Seung-bin Kim Ha-Jin Yu

Year: 2024 Pages: 2665-2669

DOI: 10.21437/interspeech.2024-1630

Get Full-Text PDF Get Analytical Report

Keywords:

Robustness (evolution) Computer science Speaker verification Speech recognition Speaker recognition Artificial intelligence Pattern recognition (psychology)

Metrics

Cited By

0.64

FWCI (Field Weighted Citation Impact)

Refs

0.68

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Speech Recognition and Synthesis

Physical Sciences → Computer Science → Artificial Intelligence

Speech and Audio Processing

Physical Sciences → Computer Science → Signal Processing

Improving Noise Robustness in Self-supervised Pre-trained Model for Speaker Verification

Metrics

Citation History

Topics

Related Documents

Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning

PRISM: Pre-trained Indeterminate Speaker Representation Model for Speaker Diarization and Speaker Verification

DINO-VITS: Data-Efficient Zero-Shot TTS with Self-Supervised Speaker Verification Loss for Noise Robustness

Language-Independent Speaker Anonymization Approach Using Self-Supervised Pre-Trained Models

Unsupervised Speaker Verification Using Pre-Trained Model and Label Correction