Towards Supervised Performance on Speaker Verification with Self-Supervised Learning by Leveraging Large-Scale ASR Models

Authors

Réda Dehak (ORCID: https://orcid.org/0000-0002-4078-7261)
Théo Lepage (ORCID: https://orcid.org/0009-0009-0676-4099)
Victor Miara

Institutions

Sorbonne Université (FR)
École Pour l'Informatique et les Techniques Avancées (FR)

Publication Details

Published: 2024-09-01
DOI: https://doi.org/10.21437/interspeech.2024-486
Citations: 5
Primary Topic: Speech Recognition and Synthesis
Type: article
Field-Weighted Citation Impact: 1.74

Funders

Agence Nationale de la Recherche
Grand Équipement National De Calcul Intensif

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article

Towards Supervised Performance on Speaker Verification with Self-Supervised Learning by Leveraging Large-Scale ASR Models

Réda Dehak, Théo Lepage, Victor Miara

Sep 1, 2024

5 citations

Speech Recognition and Synthesis

1.74

article

Towards Supervised Performance on Speaker Verification with Self-Supervised Learning by Leveraging Large-Scale ASR Models

Réda Dehak, Théo Lepage, Victor Miara

September 1, 2024article en

5 citations

Abstract

No abstract available for this paper.

Sorbonne Université (FR), École Pour l'Informatique et les Techniques Avancées (FR)

Agence Nationale de la Recherche, Grand Équipement National De Calcul Intensif

Openalex Percentile: Top 13%

Speech Recognition and Synthesis

1.74

DOI Open Access PDF OpenAlex

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Towards Supervised Performance on Speaker Verification with Self-Supervised Learning by Leveraging Large-Scale ASR Models — Réda Dehak, Théo Lepage, et al. · (2024) | TGRS Research Map | TGRS