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Voice Command Recognition Using Biologically Inspired Time-Frequency Representation and Convolutional Neural Networks.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 998-1001, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018153
ABSTRACT
Voice command is an important interface between human and technology in healthcare, such as for hands-free control of surgical robots and in patient care technology. Voice command recognition can be cast as a speech classification task, where convolutional neural networks (CNNs) have demonstrated strong performance. CNN is originally an image classification technique and time-frequency representation of speech signals is the most commonly used image-like representation for CNNs. Various types of time-frequency representations are commonly used for this purpose. This work investigates the use of cochleagram, utilizing a gammatone filter which models the frequency selectivity of the human cochlea, as the time-frequency representation of voice commands and input for the CNN classifier. We also explore multi-view CNN as a technique for combining learning from different time-frequency representations. The proposed method is evaluated on a large dataset and shown to achieve high classification accuracy.
Assuntos
Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Voz / Redes Neurais de Computação Limite: Humanos Idioma: Inglês Revista: Annu Int Conf IEEE Eng Med Biol Soc Ano de publicação: 2020 Tipo de documento: Artigo

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: Voz / Redes Neurais de Computação Limite: Humanos Idioma: Inglês Revista: Annu Int Conf IEEE Eng Med Biol Soc Ano de publicação: 2020 Tipo de documento: Artigo