Literary Review: The Role of Language Assessment in the Call

Centers Industry

Marcial Gustavo Ordonez

, Islam Avbievich Mogomedov

and Rosa Alba Ordonez

Universidad Cristiana de Honduras (UCRISH), San Pedro Sula, Honduras

Kadyrov Chenchen State University, Grozny, Russia

Universidad Tecnológica Centroamericana (UNITEC), San Pedro Sula, Honduras

Keywords: Artificial Intelligence, Call Centers, CTS, CTM.

Abstract: The following work attempts to outline the importance of the using of artificial intelligence in the call centers

in Honduras. Recently, it is a great deal during the COVID-19 outbreak, as the demand for such services risen

dramatically. With demand increase the quality seems to drop. Hence forth, it is the main task of companies

to increase the quality of the agents to sustain their businesses. The work outlines use of artificial intelligence

to understand how correctly each letter, word and sentences are utilised during calls by agents. Some data

collection techniques and analysing algorithms can be used to indicate where agents make mistakes in their

speech. Therefore, the techniques also will be discussed in this work.

1 INTRODUCTION

Call centers are English-speaking companies since

2008, they have expanded their growth in Honduras,

with a significant contribution to 6,489 job in 2016

(La Tribuna, 2017). There are different areas, most of

them hire agents who are dedicated to areas such as

product sales, customer retention, accounts

receivable, customer recovery. These agents are hired

by recruiters and must undergo multiple exams to

obtain a position in the various companies. The exams

that are carried out to be able to work in a call center

are: time it takes to type a certain number of words on

the computer, oral, written, comprehension exams.

These exams are name assessments, an assessment

involve gathering and interpreting data for a variety

of reasons being a challenge process that usually use

questionaries and structures interviews.

After being selected for hiring, people undergo

training in which they must know the products of the

company for which they applied and the processes

they must carry out. This training usually takes a

month, where the future agent must study a manual

during the first two weeks and work is done in pairs.

In the third week they begin to conduct test calls with

real clients, where they are given extensive feedback

on strengths and opportunities for improvement. In

the last week they must continue to make real calls

and put into practice everything they have learned. It

is extensive training and some of the agents do not

make it to the end. One limiting factor is the lack of

data with detailed annotations for language

assessment experiments (Shi, 2013).

People who works in call center should present

themselves as neutralized near native speakers of

some standard English (Forey, 2010). As a result of

the covid pandemic and a neutralized speaking, the

demands of consumers around the world have

increased and, therefore, the services offered by call

centers have become crucial to the new normal (La

Prensa, 2020).

However, some of the biggest problems it has had

is the constant complaints from users about the

agents' accents. And that is where many of the call

center companies have begun to use artificial

intelligence to measure the English level of their

employees. In these applications, a logarithm is used

in which it indicates at what level the pronunciation

of the words is, which letters are being pronounced

incorrectly and which is being pronounced correctly.

In this way, what is intended is to raise the level of

English of the agents. In Honduras there are more

than 450,000 unemployed people, according to the

Ministry of Labor, so a better business climate is

urgently needed to attract investment, say authorities

from the Honduran Council of Private Enterprise (El

Heraldo, 2021).

128

Ordonez, M., Mogomedov, I. and Ordonez, R.

Literary Review: The Role of Language Assessment in the Call Centers Industry.

DOI: 10.5220/0011608300003577

In Proceedings of the 1st International Conference on Actual Issues of Linguistics, Linguodidactics and Intercultural Communication (TLLIC 2022), pages 128-130

ISBN: 978-989-758-655-2

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

The ubiquity of technology has also affected

language assessment and engendered the field of

computer-assisted language testing (CALT),

dedicated to exploring how computers and

technology can be utilized for evaluating different

language skills of non-native speakers (Suvorov,

2013).

2 THEORICAL FRAMEWORK

We strongly encourage authors to use this document

for the preparation of the camera-ready. Please follow

the instructions closely in order to make the volume

look as uniform as possible (Moore and Lopes, 1999).

Please remember that all the papers must be in

English and without orthographic errors.

Do not add any text to the headers (do not set

running heads) and footers, not even page numbers,

because text will be added electronically.

For a best viewing experience the used font must

be Times New Roman, on a Macintosh use the font

named times, except on special occasions, such as

program code (Section 2.3.8).

2.1 Cognitive Translation Studies and

Computer Aided Language

Learning

For AI-based language assessment, several

techniques can be found in the literature. Some of

these techniques facilitate the development of

different programming algorithms to improve user

evaluations. It is necessary to apply cognitive

translation studies (CTS). This CTS consist in the

understanding of how the human brain learns and

achieve more knowledge will develop the mind the

ability to make translations and interpretations in a

more gradually shape (Xiao, 2020). The way people

achieve more information (vocabulary) is biggest

problem learning a new language. A computer aided

language learning (CALL) can help students to learn

in a way that does not feel too aggressive the

environment that permits to have more experiences to

develop more skills before to start in a difficult

scenario (Garrett, 2009).

2.2 Cognitive Translation Model and

Machine Translation

Two important algorithms are often used to develop

software’s solutions, the cognitive translation model

(CTM) and the machine translation (MT). The CTM

is a cognitive translation model it is an algorithm to

help develop a better recognition, identification that

will help to replicate the human cognitive-linguistic

skills. And the MT is other tool to help to achieve

great results of a cognition to replicate the tree of

human thinking, and in this way to perform a better

communicative skill of the student (Gorbis, 2019).

3 THEORICAL FRAMEWORK

When carrying out a literary review of the call center

industry, it was observed that the most important

language for this industry is English one author show

interest in studies of multilingual call centers

(Woydack). All the investigations showed that the

assessments are carried out through some computer

system, only one talks about the disadvantage of

using this systems (Douglas, 2007).

Dr. J. Lockwood is the author who appears most

in topics related to call center and language

assessment (Lockwood, 2012; Lockwood, 2015). The

countries that study this topic the most are the

Philippines with 38% followed by China with 25%,

as shown in figure 1. On the other hand, no research

was found on Honduras, even though artificial

intelligence applications are carried out in other

sectors such as agribusiness (Caballero, 2020;

Fernandez, 2021), healthcare (Sorto, 2020) and

logistics (Avila, 2020).

Some papers show interesting results about using

computer base assessments for example the

demonstration of s a viable alternative to using

features based on content models trained on large sets

of pre-scored responses (Evanini, 2013). The

qualitative analysis of communication problems that

impact call centers quality and universal language

functions (Xu, 2010; Friginal, 2008).

Figure 1: Call Centers Countries.

Call Centers Countries

Manila China USA Austria Phillipine

Literary Review: The Role of Language Assessment in the Call Centers Industry

129

4 CONCLUSIONS

Call centers industry is growing up in Honduras it is

important to develop more technical assessments to

improve and prepare more people for these jobs.

Although there are some artificial intelligence

applications in the country, there is no research for

strengthen the learning of other languages. As future

research, the development of an algorithm that

evaluates the level of English and the influence of

accents is proposed to better prepare students for the

call center technician career.

REFERENCES

La Tribuna, 2017. Call Centers un servicio en continuo

crecimiento.

Shi, Q., Li, K., Zhang, S., Chu, S. M., Xiao, J., Ou, Z., 2013.

Spoken English Assessment System for Non-Native

Speakers Using Acoustic and Prosodic Features. p. 4.

Forey, G., Lockwood, J., 2010. Globalization,

Communication and the Workplace: Talking Across

The World.

La Prensa, 2020. Pandemia hace aumentar servicios de

'call centers.

El Heraldo, 2021. La otra cara de la cuarentena: los

hondureños que no tienen ingresos durante la

emergencia.

https://www.elheraldo.hn/especiales/coronavirus/la-

otra-cara-de-la-cuarentena-los-hondurenos-que-no-

tienen-ingresos-PAEH1371349.

Suvorov, R., Hegelheimer, V., 2013. Computer-Assisted

Language Testing. The Companion to Language

Assessment. pp. 594-613.

Xiao, K., Martín, R. M., 2020. Cognitive Translation

Studies: Models and methods at the cutting edge.

Linguistica Antverpiensia, New Series – Themes in

Translation Studies. 19.

Garrett, N., 2009. Computer-Assisted Language Learning

Trends and Issues Revisited: Integrating Innovation.

The Modern Language Journal. 93. s1. pp. 719-740.

Gorbis, B., Hills, B., 2019. Cognitive Machine Translation

Model: POVs and Musketeer Classes as Algorithms of

Shared Reality. p. 8.

Woydack, J. Language management and language work in

a multilingual call center: An ethnographic case study.

Revista Internacional de Organizaciones. 23.

Douglas, D., Hegelheimer, V., 2007. ASSESSING

LANGUAGE USING COMPUTER TECHNOLOGY.

Annual Review of Applied Linguistics. 27. pp. 115-132.

Lockwood, J., 2012. Are We Getting the Right People for

the Job? A Study of English Language Recruitment

Assessment Practices in the Business Processing

Outsourcing Sector: India and the Philippines. The

Journal of Business Communication (1973). 49. 2. pp.

107-127.

Lockwood, J., 2015. What does the Business Processing

Outsourcing industry want from English language

assessment? Prospect. 23. 2. pp. 60-75.

Caballero, E. M. T., Duke, A. M. R., 2020. Implementation

of Artificial Neural Networks Using NVIDIA Digits

and OpenCV for Coffee Rust Detection. 5th

International Conference on Control and Robotics

Engineering (ICCRE). pp. 246-251.

Fernandez, O. A., Ordóñez-Ávila, J. L., Magomedov, I. A.,

2021. Evaluation of parameters in a neural network for

detection of red ring pest in oil palm. AIP Conference

Proceedings. 2442. 1. p. 030015.

Sorto, A., Marquez, T., Carrasco, A., and Ordoñez, J., 2020.

Face Recognition and Temperature Data Acquisition

for COVID-19 Patients in Honduras. J. Phys.: Conf.

Ser. 1710. 1. p. 012009.

Avila, J. L. O., 2020. Study Case: Teleoperated Voice

Picking Robots prototype as a logistic solution in

Honduras, 5th International Conference on Control and

Robotics Engineering (ICCRE)

. pp. 19-24.

Evanini, K., Xie, S., Zechner, K., 2013. Prompt-based

Content Scoring for Automated Spoken Language

Assessment. Proceedings of the Eighth Workshop on

Innovative Use of NLP for Building Educational

Applications. pp. 157-162.

Xu, X., Wang, Y., Forey, G., Li, L., 2010. Analyzing the

Genre Structure of Chinese Call-Center

Communication. Journal of Business and Technical

Communication. 24. 4. pp. 445-475.

Friginal, E., 2008. Threats to the sustainability of the

outsourced call center industry in the Philippines:

implications for language policy. Lang Policy. 8. 1. p.

51.

TLLIC 2022 - I INTERNATIONAL CONFERENCE "ACTUAL ISSUES OF LINGUISTICS, LINGUODIDACTICS AND

INTERCULTURAL COMMUNICATION"

130