IAFOR Journal of Education: Technology in Education

Volume 12 – Issue 2 – 2024

Using AI-Powered Speech Recognition Technology to Improve English
Pronunciation and Speaking Skills
Nutprapha K. Dennis
Ubon Ratchathani Rajabhat University, Thailand

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Abstract
This study aimed to investigate the impact of AI-powered Speech Recognition Technology (AISRT) in improving English pronunciation and speaking skills among EFL learners.
Additionally, it explored the opinions and responses of EFL learners towards the use of thistechnology for pronunciation and speaking skill enhancement. The research employed a pretest/post-test design and a survey questionnaire with a multiple-choice rating scale and openended questions. The study included a sample of EFL learners who received instruction andpractice using an AI-SRT program over a designated period. Data were collected through pretest and post-test assessments to measure changes in pronunciation and speaking skills. A
survey questionnaire was administered to gather participants’ opinions and responses regarding
the program’s effectiveness and their overall experience. The data were analyzed using
descriptive statistics to examine the central tendency, variability and distribution of pre-test andpost-test scores. Comparative analysis was conducted using paired t-tests to determine thesignificance of the improvement in pronunciation and speaking skills. Qualitative data fromthe survey questionnaire were analyzed thematically to identify recurring themes and patterns
in participants’ opinions. The findings of this study contribute to the understanding of the
impact of AI-powered speech recognition technology on EFL learners’ pronunciation andspeaking skills. The results reveal the effectiveness of such technology and provide valuable
insights into learners’ perceptions and experiences. This research has implications for language
educators, curriculum designers, and developers of educational technology in designingeffective pronunciation and speaking skill enhancement tools for EFL learners.
Keywords: AI-powered speech recognition technology, EFL, English pronunciation, English
speaking skills, pre-test/post-test design, survey questionnaire

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Artificial intelligence (AI) is starting to revolutionize English pronunciation and speaking. AIpowered speech recognition technologies have shown potential in assisting students inimproving their speaking skills. These programs, which are software applications or systems
that employ AI algorithms and machine learning, convert spoken language into text. They arecapable of analyzing and comprehending human speech, enabling devices or applications to
respond to voice commands, transcribe audio recordings, or provide real-time, speech-to-text
services.
While Speechling, Duolingo, and Google Assistant are prominent examples of AI-powered
speech recognition technologies, it is important to explore additional alternatives to furnish
learners with a diverse array of tools and resources (Garca-Sánchez, Soler-Urzúa, & Nussbaum,2020). This study delves into various AI-powered speech recognition programs that are
beneficial for improving English pronunciation and speaking abilities.
The use of AI technology has become more and more popular in language teaching. A
significant number of studies have been done on how AI-powered speech recognition systems
improve speaking and pronunciation. AI-based pronunciation feedback has been demonstrated
by Garca-Sánchez and colleagues (2020) to enhance non-native English speakers’
pronunciation. Similar increases in language learners’ speaking fluency were observed by Zou
and colleagues (2021) when they used AI-powered speech recognition.
The current study focuses on the implications of employing the Speechling program, an AIpowered speech recognition technology for English as a Foreign Language (EFL) learners,
especially regarding pronunciation and speaking. Speechling is an online platform to help
individuals improve their foreign language skills. The application uses artificial intelligence(AI) to deliver individualized pronunciation and fluency feedback. Learners can practice withactual human voices while recording sentences and receive expert guidance. It uses spaced
repetition algorithms to improve language skills and is completely free.
The study aims to bridge the research gap by evaluating the effectiveness of AI-SRT in English
pronunciation and speaking practice. Factors such as user-friendliness, program accuracy,
feedback mechanisms, and additional functionalities will be explored. This inquiry will aid
educators and students in selecting optimal tools for their requirements (Chen & Duan, 2020).
Moreover, the study will explore the advantages and impact of speech recognition tools driven
by AI. By broadening knowledge and comprehension of AI-SRT programs, students can makeinformed decisions in selecting the most suitable one for their personal learning objectives (Jiaet al., 2019). Finally, this study seeks to empower language learners, educators, and researchers
to harness AI technology effectively for practicing pronunciation and speaking skills. By
evaluating AI-SRT programs for English improvement, learners can discover accessible
language learning solutions tailored to their preferences. The ultimate goal is to assist students
in achieving superior English speaking and pronunciation capabilities.

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Conceptual Framework
The theoretical underpinnings of this research were grounded in well-established theories oflanguage acquisition, second language learning, and computer-assisted language learning(CALL). To inform the design and implementation of AI-powered speech recognition
technology (AI-SRT) programs for English pronunciation and speaking instruction, the study
drew on Krashen's Input Hypothesis (Krashen, 1981) and Vygotsky's Sociocultural Theory(Vygotsky, 1934). These frameworks provided a robust theoretical base for understanding the
cognitive and social dynamics of language learning and the advantages of integrating
technology in language instruction. By incorporating these established theoretical perspectives,
the study set a strong foundation for evaluating the effectiveness of the Speechling programand its impact on improving the pronunciation accuracy and speaking proficiency of language
learners.
Background of the Study
Pronunciation and speaking present continual challenges for language learners, who have tonavigate the complexities of linguistic and cultural integration to communicate accurately. The
role of AI-powered speech recognition technology in language education has been a subject ofinterest, especially as it offers real-time feedback on pronunciation and speaking. Through thecapabilities of machine learning and natural language processing, AI-SRT is able to pinpoint
errors, mispronunciations, and areas in need of improvement within a learner's spoken language(Jia & Zhang, 2018). This advanced technology can facilitate personalized feedback, enabling
tailored practice that enhances pronunciation, fluency, and overall speaking skills. Empiricalevidence has suggested that AI-SRT could be instrumental in advancing language proficiency,with Computer-Assisted Pronunciation Training (CAPT) showing notable benefits for language
learners (Jia & Zhang, 2018).
While existing studies yielded promising insights, there remains a need for further study into
the application of AI-SRT in language learning. Additional study was essential to address
potential limitations, refine its deployment, and verify its constructive contribution to language
learning outcomes. This is important as its potency in augmenting pronunciation and speaking
competencies for specific language functions has gained recognition in the realm of second
language acquisition.
Moreover, the insights gleaned from such investigations have the potential to influencecurriculum design and teaching methodologies. This study sought to delve into the influenceof AI-SRT on the pronunciation and speaking abilities of language learners. Furthermore, it
aimed to provide language educators and instructional designers with valuable guidance on
how to effectively incorporate AI-powered tools in language education programs.

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Statement of the Problem
The process of learning may have been impeded by limited access to high-quality learning
tools and a shortage of experience between teachers with technology. These challenges may
obstruct the effective practice of English pronunciation by EFL students. Therefore, thepurpose of this study was to determine whether Speechling, an AI-powered speech recognition
technology program, facilitated the accurate pronunciation of English by EFL learners.
Furthermore, the study explored the manner in which EFL learners utilized an AI-SRT program
to enhance their English pronunciation and speaking skills. This research supported educatorsin the implementation of a user-friendly and straightforward technology to enhance self-study,
pronunciation, and speaking practice.
Research Questions
The study was designed to address two research questions:
1) What is the effectiveness of an AI-SRT program in enhancing EFL learners' English
pronunciation and speaking abilities?
2) What are EFL learners’ perceptions and responses to the use of AI-SRT for English
pronunciation and speaking practice?
Purposes of the Study
This research is dedicated to exploring the potential benefits of utilizing an AI-powered speech
recognition technology program to improve the English pronunciation and speaking
proficiency of EFL students learning English. The study sets out to examine two principal
areas.
The first area of focus is to assess the impact of the AI-SRT program on the participants' ability
to enhance their English pronunciation and speaking skills. The research will employ
systematic evaluation and in-depth analysis to determine the extent to which the AI-SRT
program supports learners in the precise articulation of English words and phrases, as well as
assesses its contribution to the overall advancement of their spoken language abilities.
The second aim is to explore EFL students' perceptions and reactions to the utilization of the
AI-SRT program as a means to practice English pronunciation and speaking. By soliciting
feedback from participants and conducting interviews and surveys, the study seeks to uncoverlearners' attitudes and opinions about the integration of AI-SRT technology into their language
education. An understanding of their experiences will offer valuable insights into the program's
receptiveness and the ease with which learners can engage with this educational technology.

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Significance of the Study
This research expands the literature on using AI-powered speech recognition technology to
improve English pronunciation and speaking for EFL learners. It aims to assist language
instructors integrate such technologies into their teaching. The study will evaluate AI-SRT in
language education to assist learners develop their language abilities without advanced learning
resources. This study could further enhance English language instruction, improving
communication between learners and native speakers. The study will examine AI-SRT's
effectiveness for enhancing pronunciation and speaking skills, advancing English language
learning. This could optimize language learning by using AI technology to improve teaching
methods.
The findings could also assist curriculum developers develop more inclusive and adaptablelearning materials for diverse learners. AI-SRT's practical benefits are demonstrated in the
study to encourage policymakers to support the integration of advanced technologies in
educational settings, which could lead to more language teaching innovation.
Literature Review
Importance of Pronunciation and Speaking Skills in Language Learning
Mastering pronunciation is essential for effective communication in a second language. Studies
underscore the necessity of incorporating teaching of pronunciation, which includes elements
such as individual sounds, intonation patterns, and speech flow, within language teaching
programs to develop well-rounded oral abilities (Kang, 2016; Major, 2016).
Research conducted by Munro and Derwing (2016) and Saito and Lyster (2019) showed thatphonetic training, activities that emphasize linguistic form, and constructive correction are key
components in improving speech pronunciation. Furthermore, Thomson (2018) highlighted theneed for dedicated instruction for educators in the art of teaching pronunciation, while also
advocating for the integration of effective pronunciation instruction techniques into language
education programs to aid in more successful language learning and oral proficiency.
Bohn and Munro (2020) delved into the difficulties that arise in learning how to pronouncewords in a second language, shedding light on the obstacles students encounter and the targeted
instruction needed to surmount them. Trofimovich and Isaacs (2019) argued for an increased
emphasis on pronunciation within language teaching to enhance communicative skills. In
addition, Zhang and Wei (2019) explored how second language acquisition, speaking abilities,
and learner anxiety are interrelated, suggesting that fostering a relaxed learning environment
can boost speech development. The collective findings of these studies make a compelling casethat the role of pronunciation in language learning is pivotal, and requires personalized teachingstrategies that build student confidence and skill. Recognizing the emotional aspects oflanguage learning, such as anxiety, is also vital in crafting an educational setting that addresses
personal learning hurdles and also advances overall language fluency.

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Scholarly work emphasizes the importance of accurate pronunciation in second language
acquisition and effective communication. To fully understand a language, teaching mustaccount for sound subtleties, stress and intonation patterns, and speech flow. The relevance of
these strategies across learning settings, and the ability to accommodate learners' diverse needs,
such as native language influences, age, and unique cognitive approaches, vary and are subject
to debate. Language teaching requires nurturing learning spaces and emotional support, as wellas technical skills. Academics also want more pronunciation training for educators. Efforts tointegrate pronunciation into academic curricula must be balanced against syllabus design,
teaching materials, and educational institution strategy. Thus, while pronunciation is essential
to language learning, its instruction requires overcoming logistical and learner-related
obstacles.
Technology-Assisted Pronunciation and Speaking Training
Recent studies have shown a growing endorsement for the use of technology in the teaching ofpronunciation and speaking skills for language learners. Research conducted by Morris (2020),
Garca-Sánchez and colleagues (2020), and Liao and Xue (2019) have revealed the positiveeffects of artificial intelligence (AI) and Computer-Assisted Pronunciation Training (CAPT) in
advancing learners' abilities to pronounce accurately and speak more proficiently. These
technological tools, especially noted for their role in the betterment of vowel sounds andgeneral spoken language performance, underscore the benefits of integrating digital aids into
language education.
However, the success of technology-assisted pronunciation and speaking training is influenced
by a variety of factors. Chen and Duan (2020) and Zou and colleagues (2021) highlighted thecritical role of learner motivation, engagement, and individual learning differences in theefficacy of these programs. The literature suggests that learners' personal preferences andresponses to training significantly shape the outcomes. Accordingly, CAPT programs that offer
personalized feedback and adaptive learning experiences are recommended to cater to thediverse needs of learners. These adaptive features can optimize technology-assisted training byaligning with learners' attitudes, motivational levels, and specific requirements, thereby
enhancing the overall effectiveness of such educational technology.
The literature reviewed herein supports using AI and CAPT to improve language learning
pronunciation. However, a thorough analysis reveals that their efficacy depends on various
factors. These factors include software quality, training program design, integration into
comprehensive curricula, and adaptability to diverse learner needs and styles. Personalized
systems require resources, and learners and instructors may have different technological skills.
Although technology provides consistent feedback, it may lack the detail of human instructors.
To improve language acquisition, technology-based language training must be carefully
weighed against individual variations, learner attitudes, and instructor responsibilities.

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AI-powered Speech Recognition Technology for Language Learning
Research examining AI-SRT suggests they offer substantial benefits for language learning.
These technologies have been shown to enhance learners' pronunciation, fluency, and alleviate
their anxiety towards language learning. For example, Li and Li (2021) affirmed that AI-driven
language learning tools significantly improve pronunciation skills. Furthermore, Zhao et al.,(2021) concluded in their meta-analysis that AI-assisted tools boost pronunciation precision,
fluency, and reduce anxiety in individuals learning a second language. Yalcin and Korkmazgil(2021) discovered that an AI-infused mobile application markedly improved English
pronunciation for EFL learners Kim (2019) observed that personalized AI-based pronunciation
lessons notably advanced the English pronunciation of Korean EFL students. Additionally, Fanand colleagues (2019) reported improvements in the spoken English abilities of Chinese EFL
learners through the use of AI. Collectively, these findings point to AI-powered speech
recognition as a promising tool for enhancing the pronunciation of language learners.
Critical analysis of the literature reviewed on AI-powered speech recognition technology inlanguage learning reveals several key areas for further study. There is a lack of discussion about
AI's potential drawbacks, such as reduced human engagement and accessibility issues. The
analysis suggests studying how the technology affects different learner demographics. Itemphasizes the need for detailed pedagogical strategies to integrate technology into language
learning. To determine the credibility and long-term effects of AI-facilitated learning, thereferenced studies' research methodologies must be carefully examined. Ultimately, itemphasizes the need for resources and help in integrating these technologies into thecurriculum and the challenges of teaching educators to use them.
Recent Developments in AI-SRT for EFL Learners (2023-2024)
Recent years have witnessed significant strides in the field of artificial intelligence (AI),particularly in its application to language learning and speech recognition technologies. Thes
following literature reviews examined studies from 2023 to 2024 that exploring the use of AIpowered tools to improve English pronunciation and speaking skills.
With a cohort of English language learners, Mohammadkarimi (2024) tested Listnr and Murf
AI pronunciation aids. A mixed-method study found that students who used these tools overtraditional teaching methods improved their pronunciation. Positive attitudes toward AI-driven
aids included increased engagement and confidence. The researchers also found difficulties in
feedback interpretation and capturing subtle pronunciation nuances, suggesting AI technology
needs improvement (Mohammadkarimi, 2024). Du and Daniel (2024) conducted a systematic
review of AI chatbots' impact on English speaking skills. Their analysis of 24 studies showedthat AI chatbots are still in their infancy, requiring further research. The results suggested AI
chatbots can boost learning, reduce speaking anxiety, and improve pronunciation. Because AI
chatbots can improve learning, English teachers, chatbot designers, and researchers should
investigate them (Du & Daniel, 2024). Persulessy and colleagues (2024) examined civilengineering students' perceptions of learning engineering English vocabulary with ELSA

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(English Language Speech Assistant). Students loved learning with AI, according to thedescriptive research. The app's feedback mechanism motivated users, indicating a bright future
for AI-assisted language learning in specialized fields (Persulessy et al., 2024).
The studies reviewed provide compelling insights into the integration of AI in language
learning, particularly in enhancing English pronunciation and speaking skills.
Mohammadkarimi (2024) presented a robust case for AI pronunciation tools, with quantitative
data to back up qualitative perceptions. However, the study's limitations in feedback accuracy
pointed to the need for more sophisticated AI algorithms capable of mimicking human-likefeedback. Du and Daniel's (2024) systematic review broadened the scope of the field byanalyzing the potential of AI chatbots across various studies. This meta-analytical approach
was helpful in understanding broader trends and collective outcomes. Nonetheless, the review
underscored the infancy of research in this domain, suggesting a vast potential for future studies
to bridge the gap in knowledge and application. The focus on specialized vocabulary learningin Persulessy and colleagues’ (2024) study added a unique dimension to the understanding ofAI's role in language education. By examining a niche area of engineering English, thisresearch offered a glimpse into the adaptability of AI tools across different disciplines.
However, the study's sample size and single application focus might limit the generalizabilityof its findings.
Collectively, these studies highlight the transformative potential of AI in language learning.
They also underscored the nascent state of this research field, advocating for continued
development and exploration of AI capabilities. Future research should aim to address thechallenges identified, such as feedback interpretation, and extend the application of AI tools toa wider range of learning environments and disciplines.
Research Methodology
To comprehensively evaluate the effectiveness of the Speechling program—an AI-powered
speech recognition technology—in enhancing English pronunciation and speaking skills, thisstudy employed a mixed-methods approach. Quantitative data was collected using a pretest/post-test design to quantitatively measure the program's impact. Concurrently, qualitative
insights were gleaned from participants' responses to a survey questionnaire, tailored to capture
their perceptions of the program.
Participants and Pilot Testing
Participants for the research were drawn from a local university's sophomore EFL students
during the Academic Year 2022. A convenient sample of 25 students, aged between 18 and 22,
was selected from the English Listening & Speaking 3 Course. They were identified as having
an intermediate level of English proficiency. Before commencing the full study, a pilot test was
conducted with a smaller, separate group of students from a similar demographic, to refine theresearch instruments. The participants for the pilot test were chosen based on their availability
and similarity to the target study group. Following the pilot, adjustments were made to the test115

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items and survey questions based on the initial feedback to enhance clarity, relevance, andengagement.
Research Instruments: Pre-test, Post-test, and Survey Questionnaire
The pre-test and post-test consisted of 30 items designed to evaluate pronunciation (20 items)
and speaking skills (10 items). The Speechling program's content informed the test items,
guaranteeing their pertinence. Each component of the test had a 30-minute completion time,with consistency in difficulty maintained across pre-test and post-test items.
The survey questionnaire, comprising 12 questions with a 5-point Likert scale and an openended question, solicited students' subjective experiences with the Speechling program. The
selection of questions was informed by the pilot test feedback to ensure they effectively
captured a comprehensive range of student experiences.
Data Collection Procedures
Data collection commenced with a thorough review of the research instruments. Participants
information and privacy were safeguarded through strategic planning and the establishment ofclear protocols. This included the distribution of consent forms, which were securely stored
along with participants' data. Only the researcher had access to the collected data to maintainconfidentiality.
During the first class, participants were introduced to the Speechling program and trained onits usage. The pre-test was administered under standardized conditions, followed by theintervention period with the Speechling program. Upon completion of the intervention, thepost-test and survey questionnaire were administered with an emphasis on honest andthoughtful completion.
Data Transcription, Coding, and Analysis
Information gathered from the assessments and questionnaires was methodically transcribedand encoded. This entailed a thorough routine of inputting data, succeeded by statisticalevaluation with the aid of suitable software tools. Descriptive statistics were utilized toencapsulate the data, and inferential statistics, including averages and standard deviations,evaluated the program's effect. The qualitative feedback underwent scrutiny to extract themes
and discern patterns, and these insights were then examined in the context of the study's
primary objectives.
Ethical Considerations
To protect participants, ethical protocols were strictly adhered to throughout the study. This
included obtaining informed consent, ensuring anonymity in data handling, and securing data

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access to authorized researcher only. These measures were paramount to ensuring the ethicalintegrity of the research.
In conclusion, the methodology was designed to be comprehensive and rigorous, incorporating
pilot testing feedback for instrument refinement, adhering to ethical standards for participant
protection, and employing a combination of quantitative and qualitative analysis to provide aholistic evaluation of the AI-SRT program's efficacy.
Results
Pre-Test and Post-Test
The step of the analysis was to delineate the outcomes of the pre-test and post-test evaluations.
These evaluations were designed to measure learners' pronunciation proficiency. The data
encompassed the performance of 25 participants
Table 1 illustrates the enhancement of pronunciation skills among 25 participants following an
educational intervention. The mean score increased from 6.40 to 13.20. The pre-intervention
score ranged from 10% to 70%, reflecting a range of abilities, while post-intervention scores
ranged from 35% to 95%, marking substantial improvement. Despite a slight increase in thestandard deviation from 3.14 to 3.30, the data suggests a more uniform improvement acrossthe group, with median scores also rising from 35% to 75%. A paired t-test confirmed thestatistical significance of these improvements, with a t-value of 7.46 and a p-value of less than.05. Hence, the progress in pronunciation abilities can be confidently attributed to theeducational intervention.
Table 1
Pronunciation Abilities Pre-Test and Post-Test Results
Test
Pre-Test
Post-Test

n
25
25

Mean
6.40
13.20

SD
3.14
3.30

Minimum
2.00
7.00

Maximum
14.00
19.00

The data underscores an improvement in pronunciation among the 25 students from the pretest to the post-test. This finding implied that educators and language instructors can enhance
students' pronunciation skills by offering more speaking opportunities, employing visual aids,
and addressing pronunciation errors promptly. A paired t-test was conducted to ascertain thesignificance of this improvement.
Table 2 displays a significant increase in the speaking skills of the 25 participants after an
educational intervention. The means scores rose from 3.28 to 7.12 out of 20. The standard
deviation remained relatively stable, shifting slightly from 1.55 on the pre-test to 1.47 on the
post-test, indicating a consistent spread of scores around the mean. The minimum scores

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increased from 3 to a higher minimum of 12, while the median scores also improved from 3 to8, showcasing the participants' improved performance.
Table 2
Speaking Abilities Pre-Test and Post-Test Results
Test
Pre-Test
Post-Test

n
25
25

Mean
3.28
7.12

SD
1.55
1.47

Minimum
3.00
8.00

Maximum
7.00
12.00

A paired t-test confirmed the statistical significance of these improvements, with a t-value of
13.11 and p<.05. Hence, the progress in speaking abilities can be confidently attributed to theeducational intervention.
General Implications and Limitations
The findings from this study offer valuable insights for language instructors aiming to bolster
speaking proficiency. The successful intervention led to a demonstrable improvement in
students' speaking skills, as shown by both descriptive and inferential statistical analyses.
However, it's important to recognize the limitations of this research. Conducted with arelatively small cohort, the study focused narrowly on a single aspect of language learning.
Future research should consider larger sample sizes and encompass additional language skills
to build upon these findings.
Despite these limitations, the investigation highlights the potential efficacy of targetedlanguage-improvement interventions, with a particular emphasis on the role of descriptive andinferential statistics in gauging their success.
Table 3 shows 25 students' improved speaking abilities, with normalized scores rising from0.33 to 0.70. The intervention improved participants' performance more uniformly, lowering
SD from 0.18 pre-test to 0.16 post-test. Adding AI-powered speech recognition to thecurriculum improved academic performance. This technology, along with personalized
instruction and immediate feedback, improves pronunciation and oral communication. The
study confirms previous research on technology-assisted language learning. However, thesmall sample size and participant homogeneity may limit the results' applicability.
Table 3
Speaking Abilities Pre-Test and Post-Test Results
Test
Pre-Test
Post-Test

n
25
25

Mean
0.33
0.70

SD
0.18
0.16

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Finally, using AI in language learning improves pronunciation and speaking. Language
teachers can provide more engaging, individualized learning experiences that improve
language acquisition by incorporating such innovations.
Results from the Survey Questionnaire
The survey results revealed a unanimous agreement that the program exercises significantly
enhanced the participants’ pronunciation, speaking abilities, and vocabulary acquisition.
Specifically, 22 (88%) students strongly believed in the program's effectiveness in improving
pronunciation, while the majority confirmed its benefits for speaking skills. This aligns withthe findings reported by Shao and colleagues (2018). Shao and colleagues (2018) and Lee andJang (2018), reported similar findings regarding speech recognition systems. All participants
acknowledged the program's role in learning new words, which echoes Kuo and colleagues’(2017) findings on vocabulary development through speech recognition technology.
Furthermore, the program was found to be engaging and attention-grabbing. Twenty-three(92%) of participants reported sustained focus during exercises. Motivation to practice English
was reported by 15 (60%) participants, supporting Liu and Yang (2019)'s discovery of speech
recognition's motivational impact. An overwhelming majority concurred that the program wasinstrumental in refining their pronunciation and bolstering their confidence in English
speaking, a sentiment found by Wang and Hisao (2018).
Every participant strongly agreed on the program's efficacy in correcting pronunciation errors,
reinforcing Zhang and colleagues’ (2018) study outcomes. The program also proved helpful inamending speaking errors, as agreed by 18 (72%) participants, mirroring Lee and Jang (2018)'s
findings. The exercises' variety and the flexibility of practicing English anywhere were highly
praised, aligning with mobile and online language learning benefits highlighted by KukulskaHulme and Shield (2008). Additionally, the program's ease of use and enjoyment factor wereemphasized, which are crucial for user satisfaction and effective learning as noted by SanzTorrent and colleagues (2018) and Jang and Wu (2018).
In summary, the Speechling program, facilitated by AI-powered speech recognition technology(AI-SRT), was unanimously recognized for its positive impact on language skills, with a strong
endorsement from the participants for its pronunciation, speaking, and vocabulary exercises,
motivational appeal, error correction capabilities, and enjoyable, accessible learning
experience.
Quantitative Results
Participants were asked what they liked best about the Speechling program. The program
received positive feedback from students for its effective approach to language learning. Key
aspects that students valued the most included the program's flexibility, allowing for practiceat any time and place, and its user-friendly interface, which enhanced the learning experience.
The inclusion of a rich vocabulary and the ability to receive immediate, precise feedback on

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pronunciation were highlighted as significant benefits, contributing to improved confidence
and speaking abilities. The program's structure, which facilitates tracking progress, alongsidea variety of accents for listening practice, was appreciated for helping students become
accustomed to different English speakers and overcome language apprehension.
Moreover, the program's adaptive difficulty levels enabled personalized progression, and theengaging speaking practice questions kept learning enjoyable. Its high-precision speech
recognition technology, which closely resembles human speech, added authenticity to thelearning process. The program also offered a vast collection of audio recordings and realistic
speaking scenarios, making it both comprehensive and practical. Affordability and easy accesswere mentioned as additional advantages for students who are mindful of their budgets.
In summary, the study highlighted the Speechling program’s effectiveness in enhancing
English pronunciation and speaking skills, supported by current research on the positive
impacts of AI-assisted language programs. The program's varied features—flexible practicesettings, diverse vocabulary, adaptive levels, and authentic speech recognition—contributed toa dynamic and effective learning journey. The study’s qualitative findings suggests thattechnology-assisted pronunciation and speaking training should be an integral part of languagelearning curricula, offering a tailored and enriched experience for learners. Further
investigation is encouraged to explore the long-term benefits, best instructional practices, andintegration strategies for such programs in diverse learning environments.
Discussion and Conclusion
The application of AI-powered speech recognition technology in enhancing English
pronunciation and speaking skills among EFL students has promising outcomes. This study,
underpinned by the interactionist approach to Second Language Acquisition, highlighted thesignificant role of meaningful interactions and targeted practice facilitated by AI-SRT. The
instant feedback and the opportunity for repetitive, focused oral practice have proven to beeffective in improving pronunciation and oral fluency, resonated with findings by Liu and Yang
(2019) and Chen and Wang (2017). The technology's role as a virtual language instructor
offering personalized feedback suggested its potential to substantially improve soundproduction and speaking skills.
EFL students' English pronunciation and speaking skills have improved with AI-SRT. This
study, based on the interactionist approach to Second Language Acquisition emphasized AISRT-facilitated meaningful interactions and targeted practice. Liu and Yang (2019) and Chen
and Wang (2017) found that instant feedback and repetitive, focused oral practice improved
pronunciation and fluency. The technology's role as a virtual language instructor providing
personalized feedback suggested it could greatly improve sound production and speaking.
Learner variables like motivation, attitudes toward technology, and prior language proficiency
affected engagement and perceptions of the technology's impact. This is consistent with thework of Kim and Lee (2019) and Sung (2018), that reported learner motivation and positive120

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attitudes toward technology improve language learning outcomes. Wang and Sun (2020) and
Gao and colleagues (2019) agreed that AI-SRT's authentic conversational practice andimmediate feedback support interactionist SLA theory and the importance of meaningfulpractice. Wu and colleagues (2018) and Zhang and team (2022) recommended seamless andpurposeful AI-SRT integration into language learning environments that aligns with
instructional design and pedagogical goals. The study recommended including speech
recognition technology in the curriculum to aid language development, provided that teachersand students receive proper training and support, as noted by Lee and Jang (2018) and Zhang
and colleagues (2022).
The current study supports AI-SRT's efficacy, but practical considerations for its integration
into language curricula must be critically examined. To keep up with learners' changing needs
and language acquisition, technology must be assessed regularly. Technology's potential as a
crutch rather than a tool for learning raises the question of how to balance it with other teaching
methods to promote independent language development. The accessibility and inclusivity of
AI-SRT must also be assessed. The technology allows anytime, anywhere practice, but
socioeconomic factors and student digital literacy may limit access. Additionally, AI-SRT's
ability to accommodate different learning preferences and disabilities has not been extensively
explored. Furthermore, the study's findings suggest that while immediate feedback isbeneficial, it is the quality and specificity of the feedback that may determine the extent of
improvement in pronunciation and speaking skills. Therefore, ongoing refinement of speech
recognition algorithms to provide nuanced feedback is crucial for the sustained effectivenessof AI-SRT.
Finally, AI-SRT may continue improving language learning, but more nuanced, longitudinalstudies are needed to determine its long-term effects and optimal integration strategies fordiverse learning contexts. This includes studying how it affects students of different proficiency
levels, learning styles, and cultures. In conclusion, the Speechling program and other AI-SRT
tools offer innovative language learning methods, but their practical application requires
careful consideration of pedagogical, technological, and learner-related factors to ensure thatthey complement traditional language instruction and serve a wide range of learners.
Conclusion
An AI-powered Speech Recognition Technology was tested to improve EFL students' English
pronunciation and speaking. The study yielded important findings.
Drawing on the interactionist approach to second language acquisition, AI-powered speech
recognition technology aided meaningful interactions and targeted practice. Students could
receive instant feedback and practice pronunciation and oral fluency with the technology.
Technology integration in language learning was also shown to be important. Students were
supported and engaged by speech recognition technology powered by artificial intelligence.
The technology provided personalized feedback and instruction as a virtual language instructor,
improving students' sound production and speaking skills.

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The study also considered how learner variables affect intervention efficacy. Learners'
engagement with AI-powered speech recognition technology and perceptions of its impact on
language learning were influenced by motivation, attitudes toward technology, and priorlanguage proficiency. The results of this study contribute to the existing literature on AIassisted language learning and shed light on the potential benefits of AI-powered speech
recognition technology for enhancing pronunciation and speaking abilities. When designingeffective language learning interventions, the study highlights the importance of considering
the dynamic relationship between language acquisition theories, technology integration, andlearner factors.
This study supports the use of speech recognition technology powered by artificial intelligence
as a valuable tool for improving EFL students' English pronunciation and speaking abilities.
The findings indicate that integrating technology into language learning environments canenhance the learning experience and result in more effective language acquisition. However,additional research is needed to investigate the long-term effects of these interventions and toaddress potential challenges associated with the use of technology in language education.
Limitation of the Study
This research study provided valuable insights, but its limitations should be considered infuture studies. First, the study only included EFL students and may not apply to learners ofother languages. AI-powered speech recognition may not work for all EFL learners due to age,
language proficiency, and culture. Second, the small sample limited generalizability. A larger,
more diverse sample could explain how AI-powered speech recognition affects pronunciationand speaking. Another drawback is self-reporting and subjective evaluations. Pre-tests, posttests, and surveys are useful, but more objective measures are needed to confirm the findings.
Expert evaluations and objective pronunciation tests would strengthen the study's findings.
The study also did not examine the long-term effects of AI-driven speech recognition on
language learning. The technology's durability can be better understood through longitudinal
student studies. The study did not address technological barriers to implementing AI-powered
speech recognition in language learning environments. To ensure technology applicability andefficacy, future research should focus on system precision, user interface design, andaccessibility.
This study illuminates how artificial intelligence-powered speech recognition technology may
improve English pronunciation and speaking. To better understand how technology affects
language learning, future research should address these limitations and expand on the findings.
Suggestions for Further Studies
This research study has a number of recommendations for future research that can be made inorder to broaden and deepen our understanding of this field. These recommendations are basedon the findings and limitations of the research study.

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The study of AI-powered speech recognition technology in language learning includes multiple
narrative threads with different perspectives on its effectiveness and use. Foundational
longitudinal studies track student progress over time. These studies examine the long-term
effects of AI-powered speech recognition technology on pronunciation, speaking, and language
proficiency. By studying this perspective, researchers can better understand how thistechnology affects language learning over time and describe its benefits.
Comparative research seeks understanding. The user compares AI-powered speech recognition
technology to other language learning methods to determine its benefits. Teachers learn how to
use this technology most effectively by analyzing different teaching methods and intervention
strategies. In complex studies, learner factor shifts are important. Researchers examine howage, language proficiency, motivation, and AI-powered speech recognition technology affectlearner performance. This study illuminates the complex interaction between individual
variations and technological intervention, resulting in customized and flexible learning
environments for a variety of learners.
The study emphasizes technological optimization. AI-powered speech recognition technology
is researched to improve its design and functionality. By assessing system clarity, user interface
layout, and user input, they strategically plan to improve the technology's usability andeffectiveness to meet learners' needs. Language acquisition is fascinating and varied whenstudied across cultures. Researchers test AI-driven speech recognition technology in different
cultures and languages. This study examines how subtle cultural influences affect technology
use and effectiveness by comparing the experiences and results of EFL students from diverse
cultural backgrounds. This research helps us understand multicultural technology use.
These recommendations can help researchers better understand how AI-powered Speech
Recognition Technology can improve EFL learners' English pronunciation and speaking skills.

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Corresponding author: Nutprapha K. Dennis
Email: nootprapa@gmail.com

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