A Comparison of Deep Learning Architectures for Automatic Gender Recognition from Audio Signals

  • Alef Iury S. Ferreira UFG
  • Frederico S. Oliveira UFMT
  • Nádia F. Felipe da Silva UFG
  • Anderson S. Soares UFG


O reconhecimento de gênero a partir da fala é um problema relacionado à análise de fala humana, e possui diversas aplicações que vão desde a personalização na recomendação de produtos à ciência forense. A identificação da eficiência e custos de diferentes abordagens que lidam com esse problema é imprescindível. Este trabalho tem como foco investigar e comparar a eficiência e custos de diferentes arquiteturas de deep learning para o reconhecimento de gênero a partir da fala. Os resultados mostram que o modelo convolucional unidimensional consegue os melhores resultados. No entanto, constatou-se que o modelo fully connected apresentou resultados próximos com menor custo, tanto no uso de memória, quanto no tempo de treinamento.


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FERREIRA, Alef Iury S.; OLIVEIRA, Frederico S.; SILVA, Nádia F. Felipe da; SOARES, Anderson S.. A Comparison of Deep Learning Architectures for Automatic Gender Recognition from Audio Signals. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 715-726. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2021.18297.