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A Method of Speech Coding for Speech Recognition Using a Convolutional Neural Network

Faculty of Mechanical Engineering and Computer Science, Institute of Computer and Information Sciences, Czestochowa University of Technology, Dabrowskiego 73, 42-201 Czestochowa, Poland
Author to whom correspondence should be addressed.
Symmetry 2019, 11(9), 1185;
Received: 16 August 2019 / Revised: 2 September 2019 / Accepted: 15 September 2019 / Published: 19 September 2019
This work presents a new approach to speech recognition, based on the specific coding of time and frequency characteristics of speech. The research proposed the use of convolutional neural networks because, as we know, they show high resistance to cross-spectral distortions and differences in the length of the vocal tract. Until now, two layers of time convolution and frequency convolution were used. A novel idea is to weave three separate convolution layers: traditional time convolution and the introduction of two different frequency convolutions (mel-frequency cepstral coefficients (MFCC) convolution and spectrum convolution). This application takes into account more details contained in the tested signal. Our idea assumes creating patterns for sounds in the form of RGB (Red, Green, Blue) images. The work carried out research for isolated words and continuous speech, for neural network structure. A method for dividing continuous speech into syllables has been proposed. This method can be used for symmetrical stereo sound. View Full-Text
Keywords: speech recognition; convolutional neural network; deep learning speech recognition; convolutional neural network; deep learning
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Kubanek, M.; Bobulski, J.; Kulawik, J. A Method of Speech Coding for Speech Recognition Using a Convolutional Neural Network. Symmetry 2019, 11, 1185.

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