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  • Open Access

    ARTICLE

    1D-CNN: Speech Emotion Recognition System Using a Stacked Network with Dilated CNN Features

    Mustaqeem, Soonil Kwon*

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 4039-4059, 2021, DOI:10.32604/cmc.2021.015070 - 01 March 2021

    Abstract Emotion recognition from speech data is an active and emerging area of research that plays an important role in numerous applications, such as robotics, virtual reality, behavior assessments, and emergency call centers. Recently, researchers have developed many techniques in this field in order to ensure an improvement in the accuracy by utilizing several deep learning approaches, but the recognition rate is still not convincing. Our main aim is to develop a new technique that increases the recognition rate with reasonable cost computations. In this paper, we suggested a new technique, which is a one-dimensional dilated… More >

  • Open Access

    ARTICLE

    A Novel Scene Text Recognition Method Based on Deep Learning

    Maosen Wang1, Shaozhang Niu1,*, Zhenguang Gao2

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 781-794, 2019, DOI:10.32604/cmc.2019.05595

    Abstract Scene text recognition is one of the most important techniques in pattern recognition and machine intelligence due to its numerous practical applications. Scene text recognition is also a sequence model task. Recurrent neural network (RNN) is commonly regarded as the default starting point for sequential models. Due to the non-parallel prediction and the gradient disappearance problem, the performance of the RNN is difficult to improve substantially. In this paper, a new TRDD network architecture which base on dilated convolution and residual block is proposed, using Convolutional Neural Networks (CNN) instead of RNN realizes the recognition… More >

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