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Multilingual voice control for endoscopic procedures

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Detalhes bibliográficos
Resumo:In this paper it is present a solution to improve the current endoscopic exams’ workflow. These exams require complex procedures, such as using both hands to manipulate buttons and pressing a foot pedal at the same time, to perform simple tasks like capturing frames for posterior analysis. In addition to this downside, the act of capturing frames freezes the video. The developed software module was integrated with the MIVbox device, a device for the acquisition, processing and storage of the endoscopic results It uses libraries developed by the PocketSphinx project to recognize a small amount of commands. The module was fine-tuned for the Portuguese language which presents some specific difficulties with speech recognition. It was obtained a Word Error Rate (WER) of 23.3% for the English model and 29.1% for the Portuguese one.
Autores principais:Afonso, Simão Pedro Oliveira
Outros Autores:Laranjo, Isabel Maria Cunha; Braga, Joel Teles; Alves, Victor; Neves, José
Assunto:Automatic speech recognition Endoscopic procedures Hidden Markov Models Pocketsphinx Sphinxtrain
Ano:2015
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso restrito
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho
Descrição
Resumo:In this paper it is present a solution to improve the current endoscopic exams’ workflow. These exams require complex procedures, such as using both hands to manipulate buttons and pressing a foot pedal at the same time, to perform simple tasks like capturing frames for posterior analysis. In addition to this downside, the act of capturing frames freezes the video. The developed software module was integrated with the MIVbox device, a device for the acquisition, processing and storage of the endoscopic results It uses libraries developed by the PocketSphinx project to recognize a small amount of commands. The module was fine-tuned for the Portuguese language which presents some specific difficulties with speech recognition. It was obtained a Word Error Rate (WER) of 23.3% for the English model and 29.1% for the Portuguese one.