1.
Bratan, Costin Andrei; Tebeanu, Ana Voichita; Bobes, Gabriela; Popescu, Ionut; Iorgulescu, Gabriela; Neagu, Liliana; Apostol, Adriana; Goian, Razvan; Dascalu, Monica; Franti, Eduard; Oproiu, Ana Maria
Using Swear Words Increases the Irritability - a Study Using AI Algorithms Journal Article
In: ROMANIAN JOURNAL OF INFORMATION SCIENCE AND TECHNOLOGY, vol. 26, no. 3-4, pp. 365-374, 2023, ISSN: 1453-8245.
Abstract | Links | BibTeX | Tags: Convolutional Neural Networks; Emotion Recognition; Irritability Level; Speech Recognition; Swearing
@article{WOS:001083522800009,
title = {Using Swear Words Increases the Irritability - a Study Using AI
Algorithms},
author = {Costin Andrei Bratan and Ana Voichita Tebeanu and Gabriela Bobes and Ionut Popescu and Gabriela Iorgulescu and Liliana Neagu and Adriana Apostol and Razvan Goian and Monica Dascalu and Eduard Franti and Ana Maria Oproiu},
doi = {10.59277/ROMJIST.2023.3-4.09},
issn = {1453-8245},
year = {2023},
date = {2023-01-01},
journal = {ROMANIAN JOURNAL OF INFORMATION SCIENCE AND TECHNOLOGY},
volume = {26},
number = {3-4},
pages = {365-374},
publisher = {EDITURA ACAD ROMANE},
address = {CALEA 13 SEPTEMBRIE NR 13, SECTOR 5, BUCURESTI 050711, ROMANIA},
abstract = {This paper presents the effects' analysis produced by the frequent use
of swearing from the perspective of irritability. The analysis was
carried out with the help of two psychological questionnaires that were
completed by the volunteers before and after the inducement of the
negative emotions and automatic recognition functions implemented by
Convolutional Neural Networks (CNN), applied for the speech signals of
two volunteer groups for whom negative emotions were induced. The CNN
architecture uses Mel-frequency cepstral coefficients (MFCCs), obtained
from the speech signal, and has 87,944 trainable parameters, the outputs
of the network being the 8 main classes of emotion detected by the
algorithm (1 neutral, 3 positive, and 4 negative). The CNN also gives
information about the negative emotion and irritability level. For the
volunteers who swore during the experiment, there is an increase of 14%
in negative emotion intensity and of 21% for the irritability level
than for the volunteers who didn't swear during the trials. The use of
this current research is the understanding that cursing causes a higher
level of irritability.},
keywords = {Convolutional Neural Networks; Emotion Recognition; Irritability Level; Speech Recognition; Swearing},
pubstate = {published},
tppubtype = {article}
}
This paper presents the effects' analysis produced by the frequent use
of swearing from the perspective of irritability. The analysis was
carried out with the help of two psychological questionnaires that were
completed by the volunteers before and after the inducement of the
negative emotions and automatic recognition functions implemented by
Convolutional Neural Networks (CNN), applied for the speech signals of
two volunteer groups for whom negative emotions were induced. The CNN
architecture uses Mel-frequency cepstral coefficients (MFCCs), obtained
from the speech signal, and has 87,944 trainable parameters, the outputs
of the network being the 8 main classes of emotion detected by the
algorithm (1 neutral, 3 positive, and 4 negative). The CNN also gives
information about the negative emotion and irritability level. For the
volunteers who swore during the experiment, there is an increase of 14%
in negative emotion intensity and of 21% for the irritability level
than for the volunteers who didn't swear during the trials. The use of
this current research is the understanding that cursing causes a higher
level of irritability.
of swearing from the perspective of irritability. The analysis was
carried out with the help of two psychological questionnaires that were
completed by the volunteers before and after the inducement of the
negative emotions and automatic recognition functions implemented by
Convolutional Neural Networks (CNN), applied for the speech signals of
two volunteer groups for whom negative emotions were induced. The CNN
architecture uses Mel-frequency cepstral coefficients (MFCCs), obtained
from the speech signal, and has 87,944 trainable parameters, the outputs
of the network being the 8 main classes of emotion detected by the
algorithm (1 neutral, 3 positive, and 4 negative). The CNN also gives
information about the negative emotion and irritability level. For the
volunteers who swore during the experiment, there is an increase of 14%
in negative emotion intensity and of 21% for the irritability level
than for the volunteers who didn't swear during the trials. The use of
this current research is the understanding that cursing causes a higher
level of irritability.