Evaluation of Automatic Speech Recognition Systems

  • Matheus Xavier Sampaio Universidade Federal do Ceará (UFC)
  • Regis Pires Magalhães Universidade Federal do Ceará (UFC)
  • Ticiana Linhares Coelho da Silva Universidade Federal do Ceará (UFC)
  • Lívia Almada Cruz Universidade Federal do Ceará (UFC)
  • Davi Romero de Vasconcelos Universidade Federal do Ceará (UFC)
  • José Antônio Fernandes de Macêdo Universidade Federal do Ceará (UFC)
  • Marianna Gonçalves Fontenele Ferreira Universidade Federal do Ceará (UFC)

Resumo


O Reconhecimento Automático de Fala (ASR) é uma tarefa essencial para muitos aplicativos, como geração automática de legendas para vídeos, pesquisa por voz, comandos de voz para casas inteligentes e chatbots. Devido à crescente popularidade desses aplicativos e aos avanços nos modelos de deep learning para transcrição de fala em texto, este trabalho tem como objetivo avaliar o desempenho de soluções comerciais para ASR que utilizam modelos de deep learning, como Facebook Wit.ai, Microsoft Azure Speech, e Google Cloud Speech-to-Text. Os resultados demonstram que as soluções avaliadas diferem ligeiramente. No entanto, o Microsoft Azure Speech superou as outras APIs analisadas.

Palavras-chave: Machine learning, experiments, analysis

Referências

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Publicado
04/10/2021
SAMPAIO, Matheus Xavier; MAGALHÃES, Regis Pires; SILVA, Ticiana Linhares Coelho da; CRUZ, Lívia Almada; VASCONCELOS, Davi Romero de; MACÊDO, José Antônio Fernandes de; FERREIRA, Marianna Gonçalves Fontenele. Evaluation of Automatic Speech Recognition Systems. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 36. , 2021, Rio de Janeiro. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 301-306. ISSN 2763-8979. DOI: https://doi.org/10.5753/sbbd.2021.17889.