1.
Bratan, Costin Andrei; Marinescu, Andreea; Terecoasa, Elena; Tebeanu, Ana Voichita; Morosanu, Bogdan; Franti, Eduard; Dascalu, Monica; Andrei, Alexandra; Tocila-Matasel, Claudia; Ionescu, Bogdan; Iana, Gheorghe; Oproiu, Ana Maria; Iorgulescu, Gabriela
In: INTERNATIONAL JOURNAL OF EDUCATION AND INFORMATION TECHNOLOGIES, vol. 19, pp. 120-127, 2025, ISSN: 2074-1316.
Abstract | Links | BibTeX | Tags: artificial voice for education; convolutional neural network; emotion intensity detection; learning outcomes; magnetic resonance imaging; mirror neurons; mirror neurons; timbre recognition
@article{WOS:001550462000001,
title = {Mirror Neurons cannot be Fooled by Artificial Voices - a study with
Implications for Education using Magnetic Resonance Imaging (MRI) and
Convolutional Neural Network (CNN)},
author = {Costin Andrei Bratan and Andreea Marinescu and Elena Terecoasa and Ana Voichita Tebeanu and Bogdan Morosanu and Eduard Franti and Monica Dascalu and Alexandra Andrei and Claudia Tocila-Matasel and Bogdan Ionescu and Gheorghe Iana and Ana Maria Oproiu and Gabriela Iorgulescu},
doi = {10.46300/9109.2025.19.12},
issn = {2074-1316},
year = {2025},
date = {2025-01-01},
journal = {INTERNATIONAL JOURNAL OF EDUCATION AND INFORMATION TECHNOLOGIES},
volume = {19},
pages = {120-127},
publisher = {NORTH ATLANTIC UNIV UNION-NAUN},
address = {991 US Highway 22, Suite 100, Bridewater, New Jersey, UNITED STATES},
abstract = {Mirror neurons have a crucial role in detecting and reproducing the
actions of others as if the observer himself were performing the
specific action. In this paper, four different audio voice files are
used to determine, in two methods, the idea that the mirror neurons can
be activated only by aAreal human original voice with a strong emotional
load. The first method is the magnetic resonance imaging (MRI), which
gives information about the brain activity regarding the specific areas
where the mirror neurons are located when the four different audio files
are listened to by a group ofAten volunteers. The second method implies
a deep learning approach, using two convolutional neural network (CNNs)
architectures, one used to recognize the timbre of the audio speaker and
the second one to determine the level of remnant (residual) emotion in
the audio files listened to by them. The four audio files used are an
audio text recorded by a Romanian actress with a specific emotion, two
different actresses' voice recordings with the same text and emotion,
and with very similar voice features to the main actress, and the last
one is an artificially generated voice using AI algorithm. The results
show a promising response from both perspectives-the hypothesis that
mirror neurons can't be fooled by an artificial voice is confirmed, and
that the intensity of emotion is higher in the original voice than the
two imitating voices.},
keywords = {artificial voice for education; convolutional neural network; emotion intensity detection; learning outcomes; magnetic resonance imaging; mirror neurons; mirror neurons; timbre recognition},
pubstate = {published},
tppubtype = {article}
}
Mirror neurons have a crucial role in detecting and reproducing the
actions of others as if the observer himself were performing the
specific action. In this paper, four different audio voice files are
used to determine, in two methods, the idea that the mirror neurons can
be activated only by aAreal human original voice with a strong emotional
load. The first method is the magnetic resonance imaging (MRI), which
gives information about the brain activity regarding the specific areas
where the mirror neurons are located when the four different audio files
are listened to by a group ofAten volunteers. The second method implies
a deep learning approach, using two convolutional neural network (CNNs)
architectures, one used to recognize the timbre of the audio speaker and
the second one to determine the level of remnant (residual) emotion in
the audio files listened to by them. The four audio files used are an
audio text recorded by a Romanian actress with a specific emotion, two
different actresses' voice recordings with the same text and emotion,
and with very similar voice features to the main actress, and the last
one is an artificially generated voice using AI algorithm. The results
show a promising response from both perspectives-the hypothesis that
mirror neurons can't be fooled by an artificial voice is confirmed, and
that the intensity of emotion is higher in the original voice than the
two imitating voices.
actions of others as if the observer himself were performing the
specific action. In this paper, four different audio voice files are
used to determine, in two methods, the idea that the mirror neurons can
be activated only by aAreal human original voice with a strong emotional
load. The first method is the magnetic resonance imaging (MRI), which
gives information about the brain activity regarding the specific areas
where the mirror neurons are located when the four different audio files
are listened to by a group ofAten volunteers. The second method implies
a deep learning approach, using two convolutional neural network (CNNs)
architectures, one used to recognize the timbre of the audio speaker and
the second one to determine the level of remnant (residual) emotion in
the audio files listened to by them. The four audio files used are an
audio text recorded by a Romanian actress with a specific emotion, two
different actresses' voice recordings with the same text and emotion,
and with very similar voice features to the main actress, and the last
one is an artificially generated voice using AI algorithm. The results
show a promising response from both perspectives-the hypothesis that
mirror neurons can't be fooled by an artificial voice is confirmed, and
that the intensity of emotion is higher in the original voice than the
two imitating voices.