The Observation of Actors’ Vocal Emotion Exercises with Deep

Learning and Spectral Analysis

COSTIN ANDREI BRATAN1,2, CLAUDIA TOCILA-MATASEL3,4,

ALEXANDRA-GEORGIANA ANDREI1, ANA VOICHITA TEBEANU5, EDUARD FRANTI2,6,

MONICA DASCALU1,2, BOGDAN IONESCU1, GHEORGHE IANA3, GABRIELA BOBEȘ7,

BOGDAN MOROSANU1, ANA-MARIA OPROIU8, GABRIELA IORGULESCU8,9

1Faculty of Electronics, Telecommunications and Informational Technology,

National University of Science and Technology Politehnica,

Splaiul Independenței 313, Bucharest 060042,

ROMANIA

2Romanian Academy Research Institute for Artificial Intelligence,

Calea Victoriei 125, Bucharest 010071,

ROMANIA

3MEDIMA Health,

Odăii 42, Otopeni 075100,

ROMANIA

4University of Medicine and Pharmacy "Iuliu Hațieganu",

Victor Babes 8, Cluj-Napoca 400347,

ROMANIA

5Departamentul Pentru Pregătirea Personalului Didactic,

National University of Science and Technology Politehnica,

Splaiul Independenței 313, Bucharest 060042,

ROMANIA

6National Institute for Microtechnologies,

Erou Iancu Nicolae 126A, Voluntari 077190,

ROMANIA

7OKaua Theater Company and Pink Stil SRL,

Bucharest,

ROMANIA

8Faculty of Medicine,

University of Medicine and Pharmacy “Carol Davila”,

Bulevardul Eroii Sanitari 8, Bucharest 050474,

ROMANIA

9Academic Society of Behavioral Sciences,

Bucharest,

ROMANIA

Abstract: - This paper presents two distinct methods that demonstrate the increased intensity of a specific

emotion when the induced emotion is trained daily for 30 days. For this study, four actors participated in a 30-

day exercise trial and were recorded each day using high-level audio equipment. The first method supporting

our hypothesis is a deep learning approach. A convolutional neural network pre-trained on Mel-frequency

cepstral coefficients analyzed the actors' recordings and delivered the intensity of the detected emotion. The

WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS

DOI: 10.37394/23209.2024.21.15

Costin Andrei Bratan, Claudia Tocila-Matasel,

Alexandra-Georgiana Andrei et al.

E-ISSN: 2224-3402

153

Volume 21, 2024

CNN tested 3,561 segments of 0.2-second length, and the results showed a higher level of intensity on the final

day of training for each participant. The second method is spectral analysis. The spectrograms generated on the

first and final days of the experiment showed that the spectral composition on the final day had a wider range of

frequencies than on the first day, further supporting our hypothesis.

Key-Words: - artifficial intelligence algorithm, convolutional neural network, emotion detection, Mel-

frequency cepstral coefficients , spectral analysis, spectrogram.

Received: May 26, 2023. Revised: December 29, 2023. Accepted: January 22, 2023. Published: March 1, 2024.

1 Introduction

Actors' voices have a strong emotional impact on

the audience. Actors do not have this ability from

birth, but acquire it after a long and persevering

practice of many diction, breathing, and stage

manifestation exercises. The efforts that the actors

make in the preparation of each show are

unimaginably great for the rest of us, and they

involve repeating all the lines thousands of times

until they are "charged" with those intense emotions

that the actors want to convey to the public. An

actor's voice sounds different after repeating a text a

thousand times compared to the first reading. The

more times the actor repeats the role he is playing,

the greater will be the emotional impact of his voice

on the audience. This is the subject of this article:

measuring the intensity of the emotions imbued in

the voices of five actors during 30 days of training

with acting vocal techniques. The five actors were

audio recorded daily during training and the audio

files were analyzed with AI algorithms to determine

the intensity of emotions imbued in their voices.

Additionally, on the first and last day of training, the

actors were monitored with AI algorithms to

identify brain processes that favored the

amplification of their power of emotional influence

through the voice.

Some psychologists claim that, through the

techniques they practice during rehearsals, the

actors enter a special mental state that makes for

them to be in full unison between what they say,

think, intend, and feel, and the stage movements

they perform. This state is called in psychology the

state of flow and it helps the actors to have a strong

emotional impact on the audience, [1], [2], [3], [4],

[5], [6], [7], [8], [9], [10].

Some authors argue that the intense emotions

that the actors transmit in the state of flow are due to

the functioning mechanisms of the mirror neurons in

the brains of the spectators. Mirror neurons are

connected to those areas in the brain that are

responsible for emotions and perceptions through

the senses. Thanks to these neurons, everything we

hear, see, or perceive in another person is duplicated

in our brain along with their emotional state. The

duplication of another person's emotional state in

our brain is all the stronger the more we have

perceptions of them (auditory, visual, olfactory,

etc.). This perhaps explains why the emotions

generated by the actors in a 3D film are much more

intense than those conveyed by the actors in a silent

film, [11].

2 The State of Flow

The state of flow, also known as being "in the

zone," is a psychological concept that describes a

highly focused and fully immersive mental state in

which a person is fully engaged and completely

absorbed in an activity. Coined by psychologist

Mihaly Csikszentmihalyi, flow is characterized by a

sense of energized focus, intense concentration, and

enjoyment in the present moment. When in a state

of flow, individuals often lose track of time and

experience a deep sense of satisfaction and

fulfillment. Flow is commonly linked to activities

that push a person's abilities and skills all the while

offering objectives and instant feedback. It often

happens when the difficulty of a task aligns, with or

slightly surpasses an individual skill level resulting

in an equilibrium that enables them to perform. The

experience of flow is characterized by a sense of

engagement effortless focus, a blending of action

and awareness, and a lack of self-awareness.

Key characteristics that define the state of flow

include:

• Concentration: the person's attention is

completely immersed in the activity, fully

engaged and absorbed in what they're doing.

• Clear objectives and feedback: the activity has

well-defined goals and the individual receives

immediate feedback on their performance

enabling them to make adjustments and stay

focused.

• Fusion of action and awareness: the person

becomes one, with the activity seamlessly

carrying out actions without conscious effort or

overthinking.

WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS

DOI: 10.37394/23209.2024.21.15

Costin Andrei Bratan, Claudia Tocila-Matasel,

Alexandra-Georgiana Andrei et al.

E-ISSN: 2224-3402

154

Volume 21, 2024

• Altered perception of time: time seems to fly or

even stand still as the individual becomes fully

engrossed in the task losing track of the world.

• Sense of control: the person experiences a sense

of mastery and control over the activity as their

skills align, with the task's demands.

• Enjoyment and fulfillment: flow brings about a

sense of enjoyment, satisfaction, and intrinsic

motivation that stems directly from engaging in

the activity itself.

Flow can manifest in a variety of endeavors,

such as sports, creative pursuits, work, hobbies, and

everyday tasks. It is often associated with

performance and creativity because individuals in a

state of flow display heightened focus, productivity,

and innovation.

Attaining a state of flow offers benefits like

improved performance enhanced learning

capabilities increased well-being and a sense of

fulfillment. By understanding the characteristics and

circumstances that flow people can strive to create

conducive environments and engage in activities

that promote this ideal psychological state. Flow

refers to an experiential state that arises when

individuals are fully engaged in an activity that

aligns with its demands. In [12], the authors

discovered that demanding tasks elicited higher

levels of flow among those with greater fluid ability

but lower levels among those with lower fluid

ability. To truly experience a state of "flow" one

must find the balance, between their skill level and

the challenge they face. It involves maintaining

focus setting goals and immersing oneself in the

task at hand. During this state, individuals may lose

track of time. Temporarily set aside their sense of

self. Finding this sweet spot between skill and

challenge is essential for achieving flow, [13].

Additionally, a 2018 study suggests that

understanding the concept of flow can be valuable

in designing information systems (IS) that promote

optimal user interactions. With advancements, in

NeuroIS and psychophysiology, it is now possible to

assess flow levels during IS usage, [14].

Additionally, experiencing flow states can greatly

enhance performance and it is suggested that

transcranial direct current stimulation (tDCS) could

be a method to induce these states, [15].

Flow occurs when individuals meet a challenge

with the skills have clear goals focus their

concentration feel in control of their actions lose

track of time temporarily and experience a

temporary loss of self-awareness. An interesting

finding in [16], suggests that reading fiction that

individuals choose themselves and matches their

skill level can induce a state of flow. A research in

2013 proposes that flow arises when implicit

motivations are triggered by incentives within the

task itself without any conflicting explicit

motivations being activated. Moreover, it is crucial

for individuals to perceive themselves as capable of

achieving the task at hand, [17]. Another

perspective on measuring flow was presented in

[18], where the authors consider it as a combination

of engagement in the task enjoyment derived from it

and having control, over it.

The studies indicate that achieving a state of flow

requires finding the balance between challenge and

skill having clear goals maintaining focus feeling in

control and experiencing a sense of enjoyment.

Flow is a state that allows individuals to reach

their full potential and enhances their overall

experience. According to [19], everyday flow is

characterized by levels of motivation, cognitive

efficiency, activation, and satisfaction. Also, [20],

suggests a connection between flow and

mindfulness. In [21], the authors examined the

characteristics of flow in the process, while in [22],

the authors explored its role in sports, exercise, and

performance. Both studies concluded that flow leads

to peak performance and is associated with

experiences. In summary, these papers indicate that

experiencing flow offers benefits such as increased

motivation, cognitive efficiency, activation,

satisfaction, and peak performance.

Lately, there has been a growing interest, in

exploring the link between the state of flow and the

emotions conveyed through voice. Voice is

recognized as a tool for expressing emotions and

researchers have studied its ability to evoke

responses from listeners, [23].

When investigating how emotions conveyed

through voice impact the state of flow researchers

propose that emotional vocal signals play a role in

initiating and sustaining the flow experience.

Research findings indicate that adjusting

expressions can influence the induction of flow.

Studies revealed that individuals who were

emotionally primed demonstrated levels of flow

during tasks compared to those who received neutral

priming, [24]. This suggests that conveying cues

through voice has the potential to enhance

engagement and absorption in activities.

In contexts, Johnson, study discovered a

relationship between flow and emotional expression

in speech. Participants who reported experiencing a

state of flow during a writing task exhibited

variability in their expressions of emotions. This

indicates an interaction, between experiences,

creative engagement, and modulation of one's voice,

[25]. The study also found that people who

WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS

DOI: 10.37394/23209.2024.21.15

Costin Andrei Bratan, Claudia Tocila-Matasel,

Alexandra-Georgiana Andrei et al.

E-ISSN: 2224-3402

155

Volume 21, 2024

experience flow show changes in how they express

their emotions through their voices. This suggests

that there is a relationship, between experiences and

the immersive engagement of being in a state of

flow.

Looking at the connection between flow and the

transmission of emotions through voice has

implications across fields. In a 2013 study, the

authors explored how music, as a form of

expression affects stress response, [26]. Their

findings demonstrated that music can actively

regulate stress levels highlighting the potential for

using stimuli like voice to enhance and enrich the

experience of being, in a state of flow.

3 Methodology and Equipment

3.1 Methodology of the Study

In this study, four actors were monitored and audio

recorded for 30 days while practicing exercises to

amplify the emotional impact of the voice. All

actors agreed to voluntarily participate in this study

and were informed that they were to be monitored

by video and audio equipment during training. The

actors were audio-recorded for 30 days while they

interpreted the same text and aimed to charge it with

the same positive emotion and with as much

intensity as possible. The audio recordings from the

actors were then analyzed with AI algorithms and

spectral analysis equipment to determine and

monitor the intensity of the emotions imbued in the

actors' voices, and the spectral component of the

voices (what frequencies the actors' voices

contained).

Additionally, after the end of the training, other

measurements were made. After the last day, the

actors were made to read (at first glance) a new text

and to load it with emotions opposite to those from

the previous days' training. The new audio

recordings were then analyzed with AI algorithms

and spectral analysis equipment, and the results

were compared with those during the training.

3.2 Utilised Equipment

During the thirty-day experimental trial, all four

participants recorded their speech in a well-

equipped studio with special audio conditions to

obtain better and clearer voice recordings.

The recordings were made using a microphone

called CMC5 which had an MK4 capsule attached

to it. The microphone Signal, to Noise Ratio (SNR),

was measured at 80 dB indicating its ability to

capture the desired sound while minimizing

background noise interference. With a Maximum

Sound Pressure Level (SPL) of 131 dB, the

microphone could handle pressures without

distortion.

For interface, we used Apogee Symphony i/o in

this setup. It has a Total Harmonic Distortion plus

Noise (THD+N) rating of 115 dB at 22 dBu

meaning it accurately reproduces the input signal

without introducing distortion or noise. The

interface also boasts a range of 122 dB A weighted

allowing it to effectively capture both loud sounds.

This wide range facilitated the recording of details.

To facilitate the recording process we employed the

audio workstation Pro Tools System. Widely

recognized in the audio industry this software offers

an array of features and tools, for capturing, editing,

and processing audio. We strategically positioned

the Schoeps CMC5 with the MK4 capsule during

recording to ensure the capturing of our intended

source.

The Apogee Symphony i/o ensures that audio is

converted to resolution, with distortion and noise.

The microphone has a signal-to-noise ratio. Can

handle high sound pressure levels ensuring accurate

and distortion-free recordings. We used the Pro

Tools System to direct the signal through the

Symphony i/o for recording, editing, and

processing. To minimize noise and unwanted

reflections we designed the recording environment

with a treated booth that has a reverberation period

of 0.4 seconds. This creates a controlled audio

capture for specific types of recordings, like vocals

or instruments that require minimal room ambiance.

4 Results

The analysis of the audio recordings with AI

algorithms showed a continuous increase in the

intensity of emotions impregnated in the voices of

the actors during the 30 days of training.

The current application utilizes a Convolutional

Neural Network (CNN) structure, with connected

layers. These layers have been trained using the

Mel-frequency cepstral coefficients (MFCCs)

extracted from the input audio files. Each audio file

is divided into segments of two seconds and a fixed

window size is applied to extract 40 features using

the Discrete Fourier Transform (DFT). The CNN

consists of a total of 87,944 parameters. Employs a

ReLu activation function. Additionally, it includes a

dropout rate of 20% and a connected layer that

reduces output from 640 components to 8

components, [27].

These final eight elements correspond to the

emotions the algorithm can identify; Neutral, Calm,

WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS

DOI: 10.37394/23209.2024.21.15

Costin Andrei Bratan, Claudia Tocila-Matasel,

Alexandra-Georgiana Andrei et al.

E-ISSN: 2224-3402

156

Volume 21, 2024

Happy, Sad, Irritable, Fearful, Disgust, and

Surprised. Emotion levels range between 0 and 3

with increments of 0.5.

To train the CNN model mentioned above we

used 1,440 speech files from the Ryerson Audio

Visual Database of Emotional Speech and Song

(RAVDESS) dataset along with 2,800 files from the

Toronto speech set (TESS) dataset. Both datasets

contain audio .wav files sampled at a rate of 16 bits,

per sample. Have a sample rate of 48kHz. The

dataset consists of recordings, from 24 actors with

several females and males. These actors delivered

two sets of statements that were lexically similar all

spoken in a North American accent, [27].

The results of the study involving the CNN input

and the 3561 segments generated and tested are

presented in Table 1 for the first and 30th day of

training for each actor. It is evident from the table

that the intensity of the induced emotion increased

significantly from the first day of training to the last.

Table 1. The CNN results for each participant

Actor

No.

Intensity value on

the 1st day

Intensity value on

the 30th day

0.5

1.5

2.5

Figure 1 and Figure 2 plot the spectrograms on

the first and the last day of training. The spectral

analysis of the audio recordings showed that the

actors' voices contained a wider range of

frequencies on the last day of training compared to

the first day. This indicates that the breathing

techniques practiced by the actors helped them to

use all their resonant cavities during the

interpretation of the text and to charge it with more

and more intense emotions.

Fig. 1: Spectrogram on the first day of training

Fig. 2: Spectrogram on the last day of training

Following the training, the actors were instructed

to read a new text while simultaneously grappling

with emotions that were contrary to those

experienced during the initial training. As depicted

in Figure 3, the spectrogram of the new text was

examined. Employing the same spectral analysis

equipment, the new audio recordings were

scrutinized, and the findings revealed that both the

spectral composition of the voices and the emotions

conveyed therein were significantly lower than

those observed after the training with the initial text.

These measurements demonstrate that acting

techniques may effectively augment the emotional

content of the voice, provided they are

systematically practiced and sustained over an

extended period.

Fig. 3: Spectrogram of the new text interpretation

A comparison of the outcomes from the

assessment of the intensity of emotions and the

spectral composition of the voices after the

completion of training with the new text indicates

that acting techniques aid in enhancing the

emotional impact of the voice, provided they are

consistently applied and endured for a considerable

duration. It is worth noting that after being trained to

WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS

DOI: 10.37394/23209.2024.21.15

Costin Andrei Bratan, Claudia Tocila-Matasel,

Alexandra-Georgiana Andrei et al.

E-ISSN: 2224-3402

157

Volume 21, 2024

exhibit a specific type of emotion, actors often

require an equally prolonged period of training to

express other types of emotions. This can be

observed in some actors who, after portraying the

same role in a series for numerous years,

subsequently struggle to effectively portray other

roles.

5 Conclusion

The results of this study show that the practice of

acting techniques of voice, breathing, and stage

interpretation ensure the amplification of the power

of emotional influence of the voice. Practicing these

techniques helps actors to efficiently use all the

resonant cavities along the vocal tract, and their

voices thus become capable of conveying intense

emotions to the audience. Acting techniques also

help actors develop their power of concentration and

enter the state of flow, which proved to have an

important role in amplifying the emotional charge of

their voices.

The intersection of flow state and voice-carried

emotions highlights the intricate relationship

between psychological experiences and

communicative cues. Understanding how emotions

conveyed through voice contribute to the emergence

and maintenance of a flow state opens new avenues

for interventions in education, performance, and

therapeutic settings. Future research could delve

deeper into the neurological mechanisms that

underlie this connection, shedding light on the

intricate interplay between emotional processing,

cognitive engagement, and optimal performance.

References:

[1] Csikszentmihályi M., FLOW: The Psychology

of Optimal Experience. Harper and Row,

1990. ISBN: 978-0-06-016253-5.

[2] Nakamura J., Csikszentmihályi M., "Flow

Theory and Research". In Snyder CR, Lopez

SJ (eds.). Handbook of Positive Psychology.

Oxford University Press. pp. 195–206, 20

December 2001. ISBN: 978-0-19-803094-2.

[3] Csikszentmihályi M., "The flow experience

and its significance for human psychology".

Optimal experience: psychological studies of

flow in consciousness. Cambridge, UK:

Cambridge University Press. pp. 15–35, 1988.

ISBN: 978-0-521-43809-4.E.

[4] Wrigley W. J., Emmerson S. B., "The

experience of the flow state in live music

performance". Psychology of Music., 41 (3):

292–305, May 2013.

doi:10.1177/0305735611425903. S2CID

144877389.

[5] O'Neill S., "Flow Theory and the

Development of Musical Performance Skills".

Bulletin of the Council for Research in Music

Education. 141 (141): 129–134. JSTOR

40318998 – via University of Illinois Press,

1999.

[6] Csikszentmihályi M., Flow: "The Psychology

of Happiness". Rider. 1992. ISBN: 978-0-

7126-5477-7.

[7] Landhäußer A., Keller J., "Flow and its

affective, cognitive, and performance-related

consequences.". In Engeser S (ed.). Advances

in flow research. New York, NY.: Springer.

pp. 65–85, 2012, doi:10.1007/978-1-4614-

2359-1_4. ISBN: 978-1-4614-2359-1.

[8] Csíkszentmihályi M. (2004), Good Business:

Leadership, Flow, and the Making of

Meaning, Penguin Books.

[9] Bruya B., "Effortless Attention: A New

Perspective in the Cognitive Science of

Attention and action". Bradford Book.

Cambridge, Mass.: MIT Press, 2010. ISBN:

978-0-262-26943-8. OCLC 646069518.

[10] Ashinoff Brandon K., Abu-Akel A., (2021-

02-01). "Hyperfocus: the forgotten frontier of

attention". Psychological Research. 85 (1): 1–

19. doi:10.1007/s00426-019-01245-8. ISSN:

1430-2772. PMC 7851038. PMID 31541305.

[11] Chen D., Haviland-Jones J., Human olfactory

communication of emotions, Perceptual and

Motor Skills, 2000, 91 (3), pp.771-781.

[12] Payne B. R., Jackson J. J., Noh S. R., Stine-

Morrow E. A.. In the zone: flow state and

cognition in older adults. Psychology and

aging, 26 3, 738-43, 2011.

[13] Towey, C.A. Flow. The Acquisitions

Librarian, 13, 131 – 140, 2000.

[14] Knierim, M.T., Rissler, R., Dorner, V.,

Maedche, A., Weinhardt, C. (2018). The

Psychophysiology of Flow: A Systematic

Review of Peripheral Nervous System

Features. In: Davis, F., Riedl, R., vom Brocke,

J., Léger, PM., Randolph, A. (eds)

Information Systems and Neuroscience.

Lecture Notes in Information Systems and

Organisation, vol 25. Springer, Cham.

https://doi.org/10.1007/978-3-319-67431-

5_13.

[15] Gold J. R., Ciorciari J., A Review on the Role

of the Neuroscience of Flow States in the

Modern World. Behavioral Sciences, 10(9):

137, 2022, doi: 10.3390/bs10090137.

WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS

DOI: 10.37394/23209.2024.21.15

Costin Andrei Bratan, Claudia Tocila-Matasel,

Alexandra-Georgiana Andrei et al.

E-ISSN: 2224-3402

158

Volume 21, 2024

[16] Towey C. A., Katz B., Flow: The Benefits of

Pleasure Reading and Tapping Readers’

Interests. In Readers, Reading, and Librarians

(1st ed.). Routledge, 2001. doi:

10.4324/9781315862309.

[17] Schiepe-Tiska, A., In the Power of Flow: The

Impact of Implicit and Explicit Motives on

Flow Experience with a Special Focus on the

Power Domain, 2013, [Online]. https://d-

nb.info/1035502828/34 (Accessed Date:

February 21, 2024).

[18] Sodhi K., Luthra M., Mehta D., Yerkes-

Dodson Law for Flow: A Study on the Role of

Competition and Difficulty in the

Achievement of Flow. International Journal

of Education and Management Studies, 6, 95,

2016.

[19] LeFevre J., Flow and the quality of experience

during work and leisure. In M.

Csikszentmihalyi & I. S. Csikszentmihalyi

(Eds.), Optimal experience: Psychological

studies of flow in consciousness (pp. 307–

318). Cambridge University Press, 1988.

[20] Jackson S., Flow, and mindfulness in

performance. In A. L. Baltzell (Ed.),

Mindfulness and performance (pp. 78–100).

Cambridge University Press, 2016.

https://doi.org/10.1017/CBO9781139871310.

005.

[21] Biasutti M., Flow and Optimal Experience. In

M. A. Runco, & S. R. Pritzker (Eds.),

Encyclopedia of Creativity (2nd ed., Vol. 1,

pp. 522-528). London: Academic Press, 2011.

[22] Carter L., River B., Sachs M., Flow in Sport,

Exercise, and Performance: A Review with

Implications for Future Research. Journal of

Multidisciplinary Research, 5, 17, 2013.

[23] Juslin P. N., Laukka P., Communication of

emotions in vocal expression and music

performance: different channels, same code?.

Psychological Bulletin, 129(5), 770–814,

2003. doi: 10.1037/0033-2909.129.5.770.

[24] Chen M., Bargh J. A., Consequences of

automatic evaluation: Immediate behavioral

predispositions to approach or avoid the

stimulus. Personality and Social Psychology

Bulletin, 25(2), 215–224, 1999. doi:

10.1177/0146167299025002007.

[25] Johnson A. M., Eerola T., Huovinen E., Flow

as a musical emotion: A replication and

extension of Zentner and Eerola (2010).

Music Perception: An Interdisciplinary

Journal, 34(2), 219-234, 2017.

[26] Thoma M. V., La Marca R., Brönnimann R.,

Finkel L., Ehlert U., Nater U. M., The effect

of music on the human stress response, 2013,

PLoS ONE 8(8): e70156.

https://doi.org/10.1371/journal.pone.0070156.

[27] De Pinto M. G., Polignano M., Lops P.,

Semerano G., Emotions Understanding Model

from Spoken Language using Deep Neural

Networks and Mel-Frequency Cepstral

Coefficients, 2020 IEEE Conference on

Evolving and Adaptive Intelligent Systems

(EAIS), 2020, Bari, Italy, 27-29 May 2020.

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The authors equally contributed in the present

research, at all stages from the formulation of the

problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflicts of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

_US

WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS

DOI: 10.37394/23209.2024.21.15

Costin Andrei Bratan, Claudia Tocila-Matasel,

Alexandra-Georgiana Andrei et al.

E-ISSN: 2224-3402

159

Volume 21, 2024