Bibliografiske detaljer
| Hovedforfatter: |
Souza, Lucas Alcântara |
| Publication Date: |
2022 |
| Format: |
Master thesis
|
| Sprog: |
por |
| Source: |
Repositório Institucional da UFG |
| Download full: |
http://repositorio.bc.ufg.br/tede/handle/tede/12072
|
Summary: |
In practical scenarios, a speaker verification model system must be able to identify a person given audios of any durations. However, existing speaker verification systems have low performance when dealing with short-length audios. To face this problem, the MLVL (Meta-Learning Variable-Length) approach was proposed, which consists of using audios with different durations within the same episode in the meta-learning of a prototypical network. The objective is to become text-independent speaker verification more robust to the context in which the verification audio is short-length. Models trained with the MLVL approach were evaluated in three different scenarios of short-length audios, obtaining 2.55% as the lowest EER (Equal Error Rate) value. Evaluating such models in audios with longer durations, the lowest EER value obtained was 2.40%. The results surpassed those obtained by several studies in the same scenarios, demonstrating the potential practical application of the proposed MLVL approach in a voice biometrics system. |
