| Texto completo | |
| Autor(es): |
Contreras, Rodrigo Colnago
;
Campanharo, Amanda Fonseca
;
Viana, Monique Simplicio
;
dos Santos Bongarti, Marcelo Adriano
;
Guido, Rodrigo Capobianco
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2024, PT II; v. 15165, p. 18-pg., 2025-01-01. |
| Resumo | |
Recognizing people by their voice is not only interesting and challenging on its own, but also a very necessary ability in a variety of contexts, e.g. for unlocking electronic devices, for authentication in banking transactions and much more. However, current automatic voice verification technologies are vulnerable to presentation attacks due to the increasing realm of audio falsifications - also known as spoofing - that rely on artificial intelligence technologies, for example. Due to the direct connection of speech recognition with security systems and privacy concerns, the development of measures against spoofing is not only crucial but also urgently needed. In this work, we propose an experimental approach that focuses on dimensionality reduction techniques together with a classification model to detect spoofing in voice biometric systems. Our method uses a multicepstral feature extraction framework to distinguish between real and synthetic speech signals. To validate the proposed method, tests were performed using the ASVSpoof 2017 v2.0 database. Dimensionality reduction techniques such as singular value decomposition, genetic algorithms and auto-encoder were applied before implementing the support vector machine model. The model is then evaluated using the Equal Error Rate metric. For comparison, we investigated the voice liveness detection problem using machine learning models with and without dimensionality reduction strategies, observing an improvement of up to 7.11% in the model error metric. (AU) | |
| Processo FAPESP: | 23/06611-3 - Inteligência Comercial com Mineração de Dados |
| Beneficiário: | Monique Simplicio Viana |
| Modalidade de apoio: | Bolsas no Brasil - Programa Capacitação - Treinamento Técnico |
| Processo FAPESP: | 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria |
| Beneficiário: | Francisco Louzada Neto |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs |
| Processo FAPESP: | 19/21464-1 - Redes de aprendizado profundo e seleção de características para detecção de fraudes em sistemas de autenticação biométrica por voz |
| Beneficiário: | Rodrigo Colnago Contreras |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 21/12407-4 - Aprimorando os sistemas de autenticação biométrica por voz: robustez mediante disfonias de curta duração |
| Beneficiário: | Rodrigo Capobianco Guido |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 22/05186-4 - Aprimorando os Sistemas de Autenticação Biométrica por Voz: Robustez Mediante Disfonias de Curta Duração |
| Beneficiário: | Rodrigo Colnago Contreras |
| Modalidade de apoio: | Bolsas no Brasil - Programa Capacitação - Treinamento Técnico |