Marist College (UNITED STATES)
About this paper:
Appears in: INTED2023 Proceedings
Publication year: 2023
Pages: 201-209
ISBN: 978-84-09-49026-4
ISSN: 2340-1079
doi: 10.21125/inted.2023.0085
Conference name: 17th International Technology, Education and Development Conference
Dates: 6-8 March, 2023
Location: Valencia, Spain
Employees in the United States (U.S.) have found themselves at a clear disadvantage from their bilingual counterparts, as employers add jobs at a significantly faster pace for bilingual candidates than for non-bilingual workers overall. According to an article by New American Economy called “Not Lost in Translation” on the future of foreign language skills in the U.S. job market, proficiency in a second language is one of the most essential and marketable skills for a job seeker. Second language proficiency is presumed to increase wages and expand job opportunities for candidates with this skill. Absence of second language proficiency has significantly impacted job seekers’ abilities to communicate, relate, and problem-solve in industries such as the healthcare field, human services, and retail. Moreover, the COVID-19 pandemic elucidated the deficiency of bilingual or multilingual employees in the U.S., leading to a negative impact on employee-to-consumer communications and relationships. To help cross this language barrier, the American Council on the Teaching of Foreign Languages (ACTFL)’s guide in language proficiencies can be of use. The ability to converse in another language at the intermediate level (B1) can enable employees to communicate effectively and problem-solve within familiar situations for specific tasks.

Meanwhile, advancements in Natural Language Processing (NLP) have been predominantly in English, which fails to reflect an increasingly globalized world of language learning trends. Companies like Google and Facebook have recently introduced multilingual NLP models, paving the way for reducing the barrier to entry through machine learning software that is more marketable and accessible to a wider range of applications and users. More specifically, the use of Voice User Interfaces (VUI), in which users communicate with an Artificial Intelligence platform through voice and speech commands, offers valuable insight into methods for people to enhance their second language acquisition to the intermediate B1 level.

VoiceFlow is a platform that allows people to create voice-based text-to-speech tasks that can be executed on devices like Google Home and Amazon Alexa. We propose VoiceFlow has the potential to provide sufficient means to create a language learning prototype to train employees in repetitive tasks that simulate workplace interactions. The current study explores the design of a VUI software program capable of assisting language learners with successfully navigating repetitive conversational interactions in a specific context. Examples of English-to-Spanish applications within the healthcare field will be provided. This project also outlines the strengths, weaknesses, opportunities, and challenges of VUI software in computer assisted language learning (CALL). As the need for such technology grows, the long-term benefits of implementing VUI software for repetitive conversational interactions will outweigh the challenges for both the language learner and target language population.
Computer Assisted Language Learning, Natural Language Processing, Voice User Interface, Second Language Learning, Repetitive Conversational Tasks.