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Exploring the Effects of Automated Pronunciation Evaluation on L2
Students in Thailand
Simon Moxon
Walailak University, Thailand

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Abstract
A significant barrier to effective communication in a second language is the awareness andaccurate reproduction of phonetic sounds absent in the mother tongue. This study investigatedwhether the automated evaluation of phonetic accuracy using speech recognition technology
could improve the pronunciation skills of 105 (88 female, 17 male) Thai undergraduate
students studying English in Thailand. A pre-test, post-test design was employed using
treatment and control sample groups, reversed over two six-week periods. Treatment groupstudents were given access to an online platform on which they could record and submit theirspeech for automated evaluation and feedback via SpeechAce, a speech recognition interface
designed to evaluate pronunciation and fluency. Independent samples t-test analysis of theresults showed statistically significant improvement in pronunciation accuracy of students inthe treatment group when compared to those in the control group (t (89) = 2.086, p = .040, 95%
CI [.083, 3.423]), (t (89) = -4.692, p < .001, 95% CI [-5.157, -2.089]). Pearson’s correlation
analysis indicated a weak to moderate, but statistically significant correlation between
frequency of practise and pronunciation test score (r =.508, p < .001), (r = .384, p = .021). The
study has limitations as the sample group was predominantly female, and time constraints
limited students’ use of the software. Future studies should investigate possible genderdifferences and experiment with different forms of visual feedback.
Keywords: phonetics, pronunciation, speech recognition, SpeechAce

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Although considered the most important of the four language skills for second language (L2)students, impractical student to teacher ratios and a greater emphasis on teaching grammarmeans speaking is often neglected in the classroom (Gerald, 2000; Leong & Ahmadi, 2017).
To effectively correct pronunciation errors, the student must receive corrective feedback at thetime of the error (Huang & Jia, 2016), yet this can often prove to be impractical in the classroom
unless the lesson’s focus is pronunciation and class sizes are manageable. Assuming thatreceiving feedback in an environment where students felt secure and less exposed might
alleviate their anxiety, the present study investigated the influence on students’ pronunciation
skills after using online speech recognition software. The software is capable of analysing
phonetic accuracy and fluency and delivering instant evaluation and feedback. The study
sought to answer the following research questions:
1. Can the use of speech recognition software positively influence students’ English
pronunciation?
2. Is there a correlation between the frequency of use of the software and improved
pronunciation test scores?
During this research, the author taught speaking, listening, and phonetics in two-hour sessions
to class sizes in excess of 40 students, making one-on-one tutoring impractical. It was observedthat the voicing of word ending sounds, such as the /saIz/ of “exercise”, and pronunciation of
specific phonemes, such as the voiceless dental fricative “th”/θ/ as in “thin”, were problematicfor many of the students.
English major students at Walailak University have a broad range of English-speaking ability
and experience in communicating with native English speakers. Some students studied in
English programmes during compulsory education and had regular contact with native English
speakers from a relatively early age. Other students studied English in traditional programmeswith Thai staff or other non-native English speakers. However, the students with more accurate
English speaking and pronunciation skills and overall speaking confidence were notnecessarily those who studied in English programmes or with native English speakers. Possible
reasons for this may be the insufficient practice of pronunciation or a greater fear of making a
mistake in front of a native speaker. Research has shown that a significant factor in students’
speaking ability is their self-confidence (Aiello & Mongibello, 2019; Ayulistya, 2016; Leong& Ahmadi, 2017). Limited pronunciation skills can often lead to communication breakdown
and raised anxiety (Dewaele, 2007; Dewaele, Furnham, & Petrides, 2008) and a loss of selfconfidence (Donovan & Macintyre, 2004; Gilakjani, 2012). Fear of making a mistake ormispronouncing a word in the presence of their peers can lead to anxiety that prevents thestudent from speaking. Conversely, the teacher may refrain from giving correction for fear of
hurting the student’s feelings (Huang & Jia, 2016).
Considerable research has attempted to define the indicators of fluent speech (De Jong, 2018;
Ejzenberg, 2000; Lennon, 1990; Yang, 2014a). However, based on their survey of 84 L2
teachers teaching English as a Foreign Language (EFL), Tavakoli and Hunter (2018) state that
accurate pronunciation, including accent and intonation, is commonly rated as a criticalindicator of what Lennon describes as fluency in its broad sense (Lennon, 1990). With
increasing demands on students to pass standardised tests such as the International English
Language Testing System (IELTS), where pronunciation is a key descriptor of speaking ability(International English Language Testing System [IELTS], n.d.), the students’ need to practiceand receive evaluation and correction of their L2 speaking is significant. Despite this, L2
students often have limited time inside and outside the classroom in which to practise speaking

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with a native speaker (Leong & Ahmadi, 2017) or are taught by non-native speakers who may
be lacking in the required L2 teaching skills or grasp of English pronunciation(Kanoksilpatham, 2007).
Literature Review
Pronunciation Barriers
Incorrect pronunciation can often be a barrier to social interaction and lead to misunderstanding(Aiello & Mongibello, 2019; Ayulistya, 2016; Fraser, 2000; Leong & Ahmadi, 2017), which
in turn may cause anxiety and loss of confidence in the speaker (Dewaele, 2007; Dewaele etal., 2008; Gilakjani, 2012). Mother tongue influence, such as missing sounds, intonation, andtonal use, are often attributed to causing pronunciation error (Gilakjani, 2011; Jahandar,
Khodabandehlou, Seyedi, & Abadi, 2012; Lai, Tsai, & Yu, 2009; Latha & Ramesh, 2012). The
listener may also interpret such influences on pronunciation as disfluency in the target language(Brumfit, 1984; De Jong, 2018; Derwing & Munro, 2005; Ejzenberg, 2000; Latha & Ramesh,2012; Lennon, 1990; Richards, Platt, & Weber, 1985; Yang, 2014b).
Another problem that arises in terms of pronunciation accuracy and perceived accuracy fromthe listener’s perspective (Christiansen, 2011) is that the English language is no longer a single
standard. Globalisation has led to many English language strains, with different accents
producing different vowel sounds (Hariri, 2012). According to Gilakjani (2012), learners
require significant exposure to the target language to be proficient enough to speak. Still, lackof contact with native speakers and exposure to the target language often hinders their progress.
Khamkhien (2010) found that problems with Thai students’ pronunciation of English words
increased with the number of syllables, which reflects this viewpoint. Following his assessmentof pronunciation of multi-syllabic words by 90 Thai students, he suggests that the issue derives
from word stress and that teaching attention should focus on this area of phonics.
Although exposure to the native language is a significant factor in accurate pronunciation(Kenworthy, 1987), lack of contact and communicative opportunities with a native speaker iscommonplace in some Thai provinces. This issue is more noticeable in rural areas and areas
less frequented by western tourists, where the student to teacher ratio can render one-to-one
communication impractical or impossible (Ngamkaiwan, 2018). Conversely, in cases wherestudents have contact with nationals from different English-speaking countries, watch English
movies, or listen to English pop music, it is not uncommon to detect the influence of such
exposure on their pronunciation and vocabulary. While the listener may perceive different
accents acquired by the L2 students as disfluency or poor pronunciation, Hariri (2012) argues
that pronunciation accuracy is not native-like pronunciation but a measure of speech clarity.
Her study of related literature looked specifically into the role of gender on pronunciation
accuracy. While her findings regarding gender differences in accuracy of pronunciation support
other research findings (Hincks, 2003; Jahandar et al., 2012; Khamkhien, 2010), she concluded
that teaching style did not influence the gender difference.
Pronunciation Pedagogy
Fraser (2000) argues that even in the presence of other errors, such as grammar, successful
discourse can be achieved through good pronunciation. However, research has shown that theteaching of pronunciation in the classroom is often overlooked or allocated the least amount oftime and attention (Derwing et al., 2012; Derwing & Munro, 2005; Fraser, 2000; Gilakjani,
2011; Hariri, 2012), or completely absent from curricula after the introductory stage (Gilakjani,2011). When faced with teaching pronunciation, a lack of professional development,

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knowledge in incorporating pronunciation instruction into the classroom, and reduced
opportunity to implement pronunciation pedagogy based on research findings, have led toteachers depending more on textbooks and instinct than evidence-based guidance (Derwing &Munro, 2005; Derwing et al., 2012). L2 English, taught by inexperienced non-native speakers,
is also a factor in the fossilisation of incorrect pronunciation in students’ dialect(Kanoksilpatham, 2007). Derwing et al. (2012) also found notable inequalities in task andteaching between textbooks within a series and a lack of clarity in the explanation for students
and teachers. They called for greater integration of pronunciation in the various textbook tasks.
In his research, Gilakjani (2011) concluded that teachers should encourage students to practise
their English-speaking skills beyond the classroom’s confines. However, while this is good inpractice, students are often unable to make appropriate use of the English they learn in classbeyond the classroom’s boundaries (Gumbaridze, 2013). In Thailand, particularly for students
in remote and rural areas, the concept is not so readily and effectively put into practice asopportunities to converse with native English speakers may be scarce and, in the case ofpractising pronunciation, void of adequate corrective guidance. In the absence of propercorrection, speakers may be unaware of their errors, and hence, mispronunciations become
fixed in their regular discourse (Hincks, 2003; Swain & Lapkin, 1995). Hincks (2003), whoused an application called “Talk to Me (English)”, observed that an improvement inpronunciation accuracy was only noticeable in those students deemed to have poor
pronunciation attributed to their mother tongue accent.
Correcting mispronunciation and providing remedial help to students has been debatedconsiderably in the research literature. While teachers and students may agree that receiving
corrective feedback is necessary (Huang & Jia, 2016), the type of feedback and the timing of
its giving can be problematic and create barriers to accurate pronunciation (Heift & Schulze,
2007; Huang & Jia, 2016). According to Gumbaridze (2013), the schedule for and method ofgiving corrective feedback is a controversial topic regarding teaching methodology as it may
be inappropriate, intrusive, or result in demotivating the student. In terms of correcting
mispronunciation, Hincks (2003) states that the time required to evaluate pronunciation, andinconsistency between raters, are critical factors in error correction. She also notes that
uncertainty on the appropriate form of corrective feedback creates a barrier for teachers in theclassroom. Hincks states that fear of receiving corrective feedback in front of their peers may
prevent students from speaking in the classroom, an argument supported in other research(Gumbaridze, 2013; Huang & Jia, 2016; Terrell, 1997).
Pronunciation Software
With the wide use of mobile technology in the classroom, students have many tools to practiceand assess their pronunciation. Considerable research exists in the area of automatic speech
recognition (ASR) software for language learning, and findings have supported somecorrelation between its use for phonetic training and improved pronunciation (Ayulistya, 2016;
Haggag, 2018; Hincks, 2003; Lai et al., 2009; Olson, 2014). However, the research literature
also suggests a disconnect between research findings and classroom application (Derwing etal., 2012; Derwing & Munro, 2005; Olson, 2014).
ASR offers several advantages over human evaluation, such as instant feedback (Haggag,2018), reduced demand on teacher time, consistency of rater, and quantitative feedback on
accuracy to a very detailed level (Hincks, 2003). As a tool for students to self-assess theiraccuracy (Lord, 2005) and gain valuable practice time beyond class hours, ASR has much tooffer the L2 student in terms of guidance (Hincks, 2003) and accurate identification of45

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mispronounced segmentals (Srikanth et al., 2012). However, Kim (2006) expressed concern
regarding the reliability of scores generated by a computer algorithm. In cases where the ASR
software uses the Hidden Markov Model (HMM), the software compares the sounds received
with phoneme sounds stored in a database to find a probabilistic match (Hincks, 2003). While
this may have some potential for producing false positives when evaluating pronunciation
accuracy, most systems can accurately determine the percentage of deviation between thereceived and stored sounds and calculate an accuracy rating (Hincks, 2003).
Opinion on the use of ASR as a tool for practising pronunciation is mixed. According to
Haggag (2018), ASR promotes autonomous and collaborative learning while offering
automatic evaluation and feedback. In his study of 23 trainee English language teachers, healso found that user satisfaction and appreciation in using the software were high. However,
Setter and Jenkins (2005) argue that more advanced systems, such as Praat, are intended forresearchers and are too complicated for teachers and students to understand. Referring to Praat,
Brett (2004) concluded that, as the software was not intended for this form of use bypronunciation learners, the interface is not suitable and needs redesigning. Olson (2014),
however, rejects these claims and believes ASR systems, such as Praat, could be successfully
used in the L2 classroom. In his research, he found that students reported the visual
representation of the phoneme sounds, which the software generates automatically, helped
many compare the difference between the pronunciation of non-native and native speakers,
while only a few experienced problems using the software. Echoing the benefits regarding thevisual representation of sounds, Hincks (2003) found that displaying contours to represent pitchchanges was attributed to improved intonation. The software used by Hincks, “Talk to Me(English)”, can generate a waveform representation of the sound, similar to Praat, with visual
guidance regarding mouth and tongue positions. However, Hincks raises a significant concern,
pointing out that a focus on pronunciation using ASR software can lead to students deliberately
slowing their speech to emphasise on clarity, consequently having a negative impact on theirspeaking fluency.
The literature offers much debate regarding the suitability of software, such as Praat, inautonomous learning, but agreement on the effectiveness of waveform representation of sounds
generated by such systems diverges. Conversely, there is little in the literature to show thatwaveform representation alone, i.e., void of any corrective guidance, is any more informative
to the student than a numerical rating for each phoneme sound.
MethodVoice Recognition Technology
Prior to this investigation, students were encouraged to use the built-in voice recognition
features of their laptops and other mobile devices, such as the voice-to-text and voice command
accessibility features in Microsoft Windows, or translation applications such as Google
Translate. While these give some form of visual feedback on pronunciation accuracy, that is,
the text and speech are the same, they offer no corrective feedback when the text and speechdiffer. It was also found that the technology could sometimes assume the intended word,
possibly based on rhythmic patterns, or assumed context, even when mispronounced, thus
giving false feedback to the student. For example, using some translation applications withbuilt-in ASR, a phrase such as “We wish you a Merry Christmas” could be badly
mispronounced, yet the ASR could often assume and display the correct version of the text on
the screen if the rhythmic aspect of the utterance were accurate enough.

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A web-based platform was chosen as it would enable easy access for the typical devices
currently used by the students. It would also allow students to access the system remotely whileallowing the teacher to update content and monitor usage without manually updating student
devices. The SpeechAce application programming interface (API) was selected as it offered
the ability to interface with the existing web-based Learning Management System (LMS) used
for the course. It also gave feedback on each attempt at sentence, word, syllable, and phoneme
levels while enabling the recording of each feedback response in the LMS database. SpeechAcealso requires the submittal of a transcript to compare against each submitted sound, which
reduced the potential for encountering the false positives observed in sound only ASR
applications. The website communicated with the programming interface provided by
SpeechAce using a secure socket layer (SSL) (Figure 1). Ten pronunciation exercises were
programmed into the LMS each week for students to attempt. In each exercise, the LMS
presented students with a short sentence on their screen and recorded their voice while saying
the sentence. Students could review and re-record their attempt before submitting for analysis
by SpeechAce. For each pronunciation exercise attempt, the SpeechAce interface returned
detailed feedback in the form of a structured array packet. After saving it to a database, theLMS parsed each packet for the user to see on their screen (Figure 2). The system highlighted
evaluation scores below 70% in graduated shades of red to facilitate the students’ interpretationof the feedback and draw their attention to problematic pronunciation.
The LMS was programmed to offer two forms of exercise; assessment exercise, whichallowed each student to submit only a single attempt at each sentence, and practise exercise,
which allowed unlimited attempts. All students had access to the assessment exercises, but
only students in the treatment group had access to the practice exercises.
Figure 1
Process Flow of Voice Recognition Technology

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Figure 2
SpeechAce Feedback, Parsed for the User

Context and ParticipantsThis study was conducted in an English language programme within the Faculty of Liberal
Arts at Walailak University, Nakhon Si Thammarat, Thailand. The faculty has approximately
750 students studying in English, Chinese, Thai, and ASEAN programmes at bachelor’s degreelevel. Within the context of this study, the population consisted of first-year English major
students aged between 18 and 19 (n = 105, 88 female, 17 male) and studying in the English
programme.
SamplePermission was obtained from the University to complete the study before inviting students toparticipate. The risks to students participating in this study were no different from attending48

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class or routinely browsing the Internet. The entire population, 105 students, were invited toparticipate in the research and advised that their participation was voluntary and that they could
opt-out at any point during the 12 weeks. They were also informed that any scores collectedduring the study would not impact on their course grades. For generalisation purposes, aminimum sample size of 83 was calculated using the formula defined by Krejcie and Morgan(1970), with a confidence level of 95% and a 5% margin of error. Students were randomly
assigned to one of two groups, Group A and Group B, and instructed on using the website andSpeechAce interface.
Data Collection and Preparation
The research instrument compares the means of pre-test and post-test scores using t-tests(Hincks, 2003). Data were collected during two separate phases of the study and comparedusing t-test analysis. First, all students were invited to attempt an assessment exercise, which
would form their pre-test scores. Students in Group A (treatment group) were then allowed tocomplete the practise exercises at their leisure for six weeks while Group B (control group)
had no access to the system. At the end of the first six weeks, all students were invited toattempt the second assessment exercise, which would form their post-test scores for the firstphase and their pre-test scores for the second phase. The groups were then reversed (Group B
now the treatment group), and the study ran for a further six weeks before all students wereinvited to attempt the third and final assessment exercise, which would form their final posttest scores.
All data were screened for erroneous values and to exclude data from students who did notcomplete all speaking tasks for each of the three assessment exercises. Fourteen students failed
to fully complete all three assessment exercises, leaving data from 91 students usable forfurther analysis. The remaining data were then screened for violations of the assumptions of
the t-test and Pearson Correlation analysis methods. Shapiro-Wilk tests showed no significant
departure from normality for Test 1 (W(91) = .982, p = .225), Test 2 (W(91) = .990, p = .692),
Test 3 (W(91) = .980, p = .178), and the number of practise attempts for Group 1 (W(47) =.968, p = .221), and Group 2 (W(44) = .967, p = .230). No outliers were detected.
Results
Independent samples t-test analysis showed that scores for Test 1 were not significantly
different between the groups, (t (89) = 1.396, p = .166, 95% CI [-.684, 3.917]), suggesting no
significant difference in the level of pronunciation accuracy between the two groups before
treatment. The independent samples t-test was calculated to compare differences in pre-test andpost-test scores between the two groups. The t-test was significant for the first round of pretest and post-test, (t (89) = 2.086, p = .040, 95% CI [.083, 3.423]) and also the second round of
pre-test and post-test, (t (89) = -4.692, p < .001, 95% CI [-5.157, -2.089]). An additional t-test
analysis showed that the difference between Test 1 and Test 3 scores was also significantly
different between the two groups, (t (89) = -2.055, p = .043, 95% CI [-3.682, -0.570]).
Pearson’s correlation coefficient analysis was used to measure the correlation between thestudents’ frequency of practice and the difference between their pre-test and post-test scores.
The analysis was statistically significant for Group 1, (r =.508, p < .001), and Group 2 (r =.384, p = .021).

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Discussion
This research set out to address two questions regarding ASR software use on students’pronunciation accuracy. Addressing research question one, asking whether the use of speech
recognition software can positively influence students’ English pronunciation, the results ofthis study are encouraging and suggest the use of the speech recognition software did have apositive influence on students’ pronunciation test scores, which supports the findings in the
research literature (Ayulistya, 2016; Haggag, 2018; Hincks, 2003; Lai et al., 2009; Olson,
2014; Srikanth et al., 2012). In both rounds of testing, the treatment group displayed astatistically significant improvement in pronunciation accuracy scores when compared against
the control group. Comparison between Test 1 and Test 3 scores showed that, overall, bothgroups attained a level of improvement in their pronunciation accuracy with the improvement
being more noticeable in the scores for group 2.
Research question two explored the possible correlation between the frequency of use of thesoftware and improved pronunciation test scores. Although the correlation between practise
attempts and the difference in pre-test and post-test scores was significant, the strength of thecorrelation (rxy2 = .258 for Group 1 and rxy2 = .147 for Group 2) was considered weak tomoderate (Salkind, 2012). It is proposed that repeated practise of the exact pronunciationphrase was not overly helpful in improving pronunciation accuracy because students may havebeen unable to make appropriate use of the feedback to correct their mispronunciation. This
viewpoint reflects the research literature (Brett, 2004; Setter & Jenkins, 2005). In the present
study, the feedback was given as percentages against each word, syllable, and phoneme andhighlighted in shades of red to reflect degrees of inaccurate pronunciation. While the extent towhich students examined the feedback is not apparent, limitations in the students’ phoneticalknowledge could have made it too difficult for many of them to convert the feedback into
corrective measures. The author suggests that a visual representation of the problematic
phonemes, using waveform and mouth/tongue position diagrams, such as the “Talk to me”
software used by Hincks (2003), would make it easier for students to self-correct their errors.
As animated diagrams or video instruction would be expected to offer better remedial guidance,
having the ability to directly link to such media by clicking on a phoneme in the feedback
would provide greater independence to the student in terms of pronunciation practice in theabsence of a native speaking instructor.
Another aspect that may have caused a weaker correlation between practise and test scores isthat each practice exercise consisted of an entire phrase rather than individual words orphonemes. The practise effect may have been a more significant influence if the interface had
been able to identify the problematic words and phonemes for each student and allow
individuals to practise each in isolation rather than as part of a phrase.
It is noteworthy that a decrease in pronunciation accuracy was observed in each group during
the period when they were not using the interface (Figure 3). This decrease could be attributed
to a general lack of pronunciation practise while not using the interface, which is reflective of
the findings of other studies relating to pronunciation pedagogy (Derwing et al., 2012; Derwing& Munro, 2005; Fraser, 2000; Gilakjani, 2011; Hariri, 2012).
Overall, it was found that Group 2 displayed significantly higher improvement than Group 1
between Test 1 and Test 3, (t (89) = -2.055, p = .043, 95% CI [-3.682, -0.570]). It could beargued that recency effect contributed to Group 2’s better performance in Test 3 as Group 1
had not practised with the software during the six weeks prior to the third test. However, as

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pronunciation scores for Group 2 in Test 2 were observed to decrease below what wasconsidered their baseline level (Test 1), it is felt that other factors, besides practice, may have
had an influence on student pronunciation in this study.
Figure 3
Mean Test Scores

Recommendations
A concerning observation regarding students’ pronunciation in this study was the deteriorationin their accuracy during periods when they did not have access to the practice interface. The
implications for this suggest that pronunciation practice receives insufficient classroom time,or students lack environments where they can practise independently outside of the classroom.
Students in this study had four hours of contact per week in the classroom with a native speaker,
which significantly reduces their frequency of practice and rate at which they can develop theirspeaking proficiency. As suggested by Derwing et al. (2012), the practice of pronunciation
should form an integral part of all EFL teaching rather than be taught independently from otherskills. Based on observations and the research findings, the author echoes this suggestion andrecommends that teachers adopt existing ASR applications and incorporate them into their
independent study and flipped classroom sessions. Teachers should be mindful of theirstudents’ ability to interpret and respond to feedback from ASR systems to prevent them fromfeeling incapable of improving despite repeated attempts.
Another issue raised in this research was the quality and effectiveness of corrective feedback.
ASR systems can significantly facilitate the evaluation and feedback process in terms ofprocessing time, consistency, and protecting students’ emotions. However, those systems
intended for independent study purposes need to consider the user’s ability to interpretfeedback and make the appropriate corrections to their pronunciation. In the case of this study,
the numerical feedback given by the SpeechAce interface, while adequate to identify problem

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areas, does not appear to have been sufficiently user friendly to enable the students toeffectively self-correct their mistakes. This shortfall can potentially demotivate the student if
they fail to achieve higher pronunciation scores despite multiple attempts. Future researchshould look into more effective ways for independent study ASR systems to provide corrective
feedback and how best to incorporate remedial practice tasks that pinpoint the user’s specificpronunciation problems. A more intelligent system should detect a problematic phoneme andpresent the user with remedial practice specific to it. However, a future study should also
investigate any significant benefit in presenting evaluation scores to the user in graphical or
numerical form.
Based on the unusual decrease in pronunciation accuracy observed in Group 2 during the firstround of this study, future research should investigate the factors that may lead to such reduced
accuracy, which, as proposed by the author, may go beyond insufficient pronunciation practice.
A future study should also examine potential gender differences in practising with the
SpeechAce interface.
Conclusion
Pronunciation plays an essential part in effective communication (Fraser, 2000) and is often
assessed in isolation during the standardised tests that EFL students often must complete aspart of induction screening or course evaluation processes. As discussed in the research
literature, teacher and student problems regarding feedback stem from limited contact time andhow and when teachers should give feedback. The potential for loss of face is considered aprime reason for students not wishing to receive corrective feedback in front of their peers. It
is hoped that advances in artificial intelligence software and mobile technology will create aclear pathway that offers students applications that can identify pronunciation errors andprovide corrective instruction through user-friendly interfaces.
Limitations
Although the findings in this research reflect a positive outcome of using ASR for practicing
pronunciation, two factors may have influenced the results, namely sample and time.
Limitations on transactions permitted through the SpeechAce subscription forced a restrictionin terms of the number of participants. First-year students were selected because it was felt theywould have less fossilisation of errors and were also heterogeneous in their prior pronunciation
training. However, this meant the sample was relatively small and predominantly female,which prevented any meaningful analysis by gender. Several studies in the research literaturereported gender differences in different aspects of pronunciation accuracy (Hariri, 2012;
Hincks, 2003; Jahandar et al., 2012; Khamkhien, 2010), which may also have influenced theresults in this investigation. Time constraints on this study meant that students had a relatively
short time using the technology and learning how to interpret the feedback scores to self-correct
their pronunciation errors.
Declarations
Funding
Walailak University, Thailand, funded this work. Research grant: WU63203.
Conflicts of Interest
In my ethical obligation as a researcher, I am reporting that I am employed by and received
funding from Walailak University. The University had no involvement in the design,52

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collection, analysis, or interpretation of this article’s data or its writing. The University grants
the author permission to submit this article for publication.

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Corresponding author: Simon Moxon
Email: simon@sitamoxon.com

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