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All Journal International Journal of Electrical and Computer Engineering
Baquero, Javier Eduardo Martinez
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Computer Science & IT Electrical & Electronics Engineering

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Robust identification of users by convolutional neural network in MATLAB and Raspberry Pi Murillo, Paula Useche; Jiménez-Moreno, Robinson; Baquero, Javier Eduardo Martinez
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3876-3884

Abstract

The following article presents the development of an algorithm embedded in a Raspberry Pi 3B board, where a user identification was made, using the convolutional neural network (CNN) for 5 predefined users, with the option of loading remotely a new network for a new user. Comparatively, the same application was programmed in MATLAB programming software to evaluate the results and identify the advantages between them. Networks were trained for 5 different users, using the Caffe library on the Raspberry Pi, and the MATLAB neural network package on the computer. Where it was found that the training made by Caffe on an embedded system is much slower and less efficient than the ones performed in MATLAB, obtaining less than 55% accuracy with Caffe networks and more than 90% with MATLAB networks, training with the same number of samples, the same architecture, and the same database. Finally, the accuracy obtained through confusion matrix is over 88% in each case of users identification.
Smart chatbot for surveys by convolutional networks speech recognition Jimenez-Moreno, Robinson; Baquero, Javier Eduardo Martínez; Umaña, Luis Alfredo Rodriguez
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3410-3417

Abstract

This paper details the development of an innovative voice chatbot interface specifically designed for evaluating user options using a Likert scale by color. The core of this interface is designing a convolutional neural network architecture, which has been trained with MEL spectrogram inputs from seven possible words for each answer. These spectrograms are crucial in capturing the audio features necessary for effective voice recognition and establishing the interactions that occur between the chatbot and the user, allowing the convolutional network to learn and distinguish between different types of user responses accurately. During the training phase, the convolutional neural network achieved an accuracy rate of 91.4%, indicating its robust performance in processing and interpreting voice commands. The interface was tested in a controlled environment, with a group of ten users and a survey of 5 questions, where it achieved a perfect detection accuracy of 100%. The results demonstrate the system's capacity for natural user interaction by voice and employing a free text to speech (TTS) algorithm for the chatbot voice.
Co-Authors Jimenez-Moreno, Robinson Murillo, Paula Useche Umaña, Luis Alfredo Rodriguez