VOiCES

The 'VOiCES' dataset is a benchmark used to evaluate speaker verification performance, particularly focusing on short-utterance scenarios.

Papers using VOiCES (9)

DAME: Duration-Aware Matryoshka Embedding for Duration-Robust Speaker Verification2026Self-Supervised Speech Quality Assessment (S3QA): Leveraging Speech Foundation Models for a Scalable Speech Quality Metric2025persoDA: Personalized Data Augmentation for Personalized ASR2025pMCT: Patched Multi-Condition Training for Robust Speech Recognition2022 · 7 citesDereverberation of Autoregressive Envelopes for Far-field Speech Recognition2021 · 1 citesEnd-to-End Speech Recognition With Joint Dereverberation Of Sub-Band Autoregressive Envelopes2021MultiSV: Dataset for Far-Field Multi-Channel Speaker Verification2021How to Leverage DNN-based speech enhancement for multi-channel speaker verification?2022Speech enhancement with frequency domain auto-regressive modeling2023