Meta-aprendizado para a verificação de falante com áudios de curta duração
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| Главный автор: | |
|---|---|
| Дата публикации: | 2022 |
| Формат: | Master thesis |
| Язык: | por |
| Источник: | Repositório Institucional da UFG |
| Download full: | http://repositorio.bc.ufg.br/tede/handle/tede/12072 |
Итог: | 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. |
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