findings suggest that the meaningful and controlled context of EPI could play a crucial role in improving advanced EFL learners' pronunciation skills.

To conclude, while existing research has established the positive effects of EPI across various learner populations and approaches, substantial gaps remain. In particular, learners from German language backgrounds have been largely overlooked, as have studies that focus on approximating specific EPI target varieties of English.

## 2.1 Research question and hypothesis

In light of these gaps, the following research question was investigated: How does the pronunciation of EFL university students of selected segmental features – in particular, /ɒ – α:/, /əʊ – əʊ/, and postvocalic /r/ – develop over a semester of EPI? The following hypothesis was formulated: EFL university students show significant improvement in their pronunciation of the selected segmental features after a semester of EPI.

# 3. Methodology

## 3.1 Participants and setting

The study was conducted at the Department of English and American Studies of the University of Vienna. Participants were fourth-semester bachelor students enrolled in PPOCS1, who are expected to have reached an upper intermediate B2 proficiency level (Council of Europe, 2020). PPOCS1 is a weekly two-hour class offering EPI. Prior to enrolment, students choose between one of the two offered varieties, SBE and GAE. PPOCS1 is complemented by a mandatory weekly two-hour language lab, focusing on the chosen variety.

A total of 82 students participated in the study. Of these, 45 students attended the BE PPOCS1 course, while the remaining 37 took the GA PPOCS1 course. 84% (n = 73) of the participants reported German as their first language (L1), 6% (n = 5) were bilingual German speakers, and the remaining 10% (n = 9) had other L1s, including Albanian, Arabic, Bosnian/Croatian/Serbian, Czech, Dutch, Hungarian, Polish, Romanian, and Turkish. Despite varying language backgrounds, all participants spoke German. None of the participants identified English as their L1.

## 3.2 Pronunciation instruction

Participants attended two EPI classes per week (4 hours total), receiving specific English variety instruction on segmental and suprasegmental features. PPOCS1 and the language lab combine theory with practical classroom activities, such as listening to audio guides and producing targeted features, which were shown to be effective practices (Galante & Piccardo, 2022). According to Richter (2021), the two courses aim at developing students' theoretical and practical knowledge of segmental and suprasegmental features and help them approximate one of the two offered varieties of English. In their design, the courses offer not only hands-on opportunities to practise spoken language communicatively but also regular feedback on the production of segmentals and suprasegmentals (Richter, 2021).

## 3.3 Data collection

Prior to data collection, the testing procedures were thoroughly explained to the students, and any questions were addressed to ensure clarity and comprehension. Participants provided their informed consent to participate in the study and agreed to the processing of the data. At the
beginning of the semester, students completed a questionnaire to provide information on their language background and their chosen variety for PPOCS1. Speech samples were collected at the beginning of the course in week 1 (March 2023, pre-test) and at the end of the course in week 14 (June 2023, post-test) in the language lab. At both testing points, participants were required to record themselves individually reading a text passage aloud. While reading aloud is not an optimal measure of pronunciation change, it facilitates comparability across recordings and a more consistent analysis of the targeted features.

Specifically, the text “The North Wind and the Sun” was employed due to its established use in previous research studies (e.g., Gass & Varonis, 1994) and its inclusion of all relevant features under investigation. This study focuses on three segmental features: two contrasts /ɒ – a:/ and /əʊ – əʊ/, and the postvocalic /r/, which are recognised as distinguishing characteristics between GAE and SBE and typical challenging features for German speakers (Schmitt, 2016). The text passage includes 30 words (see Table 1) featuring one or more of the targeted phonemes, which were embedded in the story to ensure that students read the words in context rather than in isolation. All speech stimuli were recorded using the software KALTURA<fnref n="1" /> and saved in MP4 format. A total of 164 recorded samples were analysed (82 at pre-test and 82 at post-test).

<caption>**Table 1**</caption>

*Distribution of 33 Selected Segmental Features*

<table><thead><tr><th>Selected segmentals</th><th>Total number of tokens</th><th>Example words</th></tr></thead><tbody><tr><td>/ɒ – a:/</td><td>6</td><td>stronger, off, stopped</td></tr><tr><td>/əʊ – əʊ/</td><td>7</td><td>cloak, closely, fold, so</td></tr><tr><td>/r/</td><td>20</td><td>North, were, stronger, traveller, dirty, first, considered, other, hard, more, warmly</td></tr></tbody></table>

**4. Data analysis and results**

Data were analysed using R version 4.3.3 (R Core Team, 2024) and RStudio version 2024.04.2 (RStudio Team, 2024); specifically, the R packages “stats” (R Core Team, 2024), “rcompanion” (Mangiafico, 2024), and “vcd” (Meyer et al., 2023).

**4.1 Raters and rating scale**

To investigate students' development in pronunciation after receiving EPI, this study used an auditory analysis, as in prior research (e.g., Saito, 2011; Zhang & Yuan, 2020). During the

<footnote n="1">¹ KALTURA https://corp.kaltunga.com/</footnote>
rating stage, four trained listeners rated the participants' performances. These listeners were considered trained due to their roles as language lab tutors at the University of Vienna, where they are regularly exposed to a wide range of EFL university students' speech and expected to provide feedback. Each listener was assigned 41 student recordings (i.e., four sets) per rating cycle, with two cycles in total to ensure inter-rater reliability. In the first cycle, each rater assessed one set, and in the second cycle, they assessed a different set, ensuring that each recording received two independent ratings.

Subsequently, a balanced rating approach was adopted, which included assigning codes to each student recording. This coding system ensured participant anonymity and prevented raters from knowing whether they were evaluating pre- or post-instruction recordings. Additionally, each rater assessed recordings from both SBE and GAE varieties, which ensured a fully crossed design and further minimised potential rating effects. Weighted Cohen's Kappa ($\kappa_w$)<fnref n="2" /> was calculated for each set of raters for all three target features. The range of $\kappa_w$ for each feature is presented here to summarise the overall agreement levels across all four sets: For /d – a:/, $\kappa_w$ values ranged from .32 to .70, indicating a relatively broad level of agreement from fair to substantial; /əʊ – oʊ/ showed moderate to substantial agreement, with $\kappa_w$ values between .50 and .74. Finally, regarding the postvocalic /r/, the agreement was consistently higher, with $\kappa_w$ values ranging from .74 to .90, reflecting substantial to almost perfect agreement across the sets.

Moreover, all raters utilised a standardised rating scale (see Appendix) developed specifically for this study to maintain consistency. Each segmental was assessed across a set number of tokens. For each token of a feature, raters determined whether the pronunciation approximated the target variety, assigning a binary score of 0 (not approximated) or 1 (approximated). The total number of correct tokens for each feature was calculated by summing the scores across all instances. This value was then converted into a percentage of accurate approximations relative to the total number of target instances for that feature. Based on the percentage of correct approximations, participants were assigned a score on a 5-point scale: not approximated to target variety (value 1), slightly approximated to target variety (value 2), moderately approximated to target variety (value 3), mostly approximated to target variety (value 4), and fully approximated to target variety (value 5).

## 4.2 Results

To investigate the significance of changes from pre-test to post-test, a Wilcoxon signed-rank test was computed for each target feature. The analysis also included the effect sizes of significant differences; this study reports the rank biserial correlation coefficient ($r_{rb}$), which is appropriate for non-parametric tests of difference (King et al., 2011). To interpret the magnitude of the $r_{rb}$, the analysis followed field-specific benchmarks for second language research proposed by Plonsky and Oswald (2014), who classify standardised r-values of .25, .40, and .60 as small, moderate, and large effect sizes, respectively.<fnref n="3" /> To reduce the risk of alpha error accumulation, the significance threshold was set at .01.

The Wilcoxon signed-rank test revealed significant improvements in students' pronunciation from the pre-test to the post-test across all target features. Table 2 presents the

<footnote n="2">² To interpret the magnitude of agreement, Landis and Koch's (1977) benchmarks were employed.</footnote>

<footnote n="3">³ While Plonsky and Oswald's (2014) field-specific benchmarks for interpreting Pearson *r* were developed based on parametric data, the conceptual similarity between Pearson *r* and rank biserial $r_{rb}$ (Rosenthal, 1991) allows for a tentative application of these thresholds. As argued by Kirby (2014), $r_{rb}$ is more appropriate for non-parametric tests as it takes into account rank sum differences. Therefore, $r_{rb}$ was expected to yield more nuanced results.</footnote>
descriptive statistics, including the median scores for each feature at both the pre-test and post-test stages; Table 3 displays the results of the Wilcoxon signed-rank test for each target feature, reporting the statistical significance and *r*<sub>rb</sub> effect sizes.

<caption>**Table 2**</caption>

Means, Medians, Standard Deviations for all Target Features during Pre-Test (T1) and Post-Test (T2)

<table><thead><tr><th>Feature</th><th>Time Point (T1, T2)</th><th><em>M</em></th><th><em>Mdn</em></th><th><em>SD</em></th></tr></thead><tbody><tr><td>/ɒ – a:/</td><td>T1</td><td>3.70</td><td>4.00</td><td>1.48</td></tr><tr><td>/ɒ – a:/</td><td>T2</td><td>4.05</td><td>5.00</td><td>1.33</td></tr><tr><td>/əʊ – oʊ/</td><td>T1</td><td>3.23</td><td>4.00</td><td>1.77</td></tr><tr><td>/əʊ – oʊ/</td><td>T2</td><td>4.29</td><td>5.00</td><td>1.17</td></tr><tr><td>/r/</td><td>T1</td><td>4.07</td><td>5.00</td><td>1.34</td></tr><tr><td>/r/</td><td>T2</td><td>4.67</td><td>5.00</td><td>0.74</td></tr></tbody></table>

Note. N = 82. **p < .01.

<caption>**Table 3**</caption>

One-Tailed Wilcoxon Signed-Rank Test Results for Target Features

<table><thead><tr><th>Feature</th><th><em>W</em></th><th><em>Z</em></th><th><em>p</em></th><th><em>r</em><sub>rb</sub></th><th>99% CI [LL, UL]</th></tr></thead><tbody><tr><td>/ɒ – a:/</td><td>351.0</td><td>-2.54</td><td>.011</td><td>-.38</td><td>[-.76, < -.01]</td></tr><tr><td>/əʊ – oʊ/</td><td>19.5</td><td>-5.41</td><td>&lt; .001</td><td>-.95</td><td>[-1, -.82]</td></tr><tr><td>/r/</td><td>108.5</td><td>-3.89</td><td>&lt; .001</td><td>-.69</td><td>[-.94, -.35]</td></tr></tbody></table>

Note. N = 82.

As shown in Table 2, for /ɒ – a:/, students performed better after the intervention (Mdn = 5.00) than before (Mdn = 4.00). The Wilcoxon signed-rank test indicated that this improvement was statistically significant, Z = -2.54, *p* = .011, with a small to moderate effect size (*r*<sub>rb</sub> = |.38|). The diphthong /əʊ – oʊ/ also improved significantly (Z = -5.41, *p* < .001), with students performing better in the post-test (Mdn = 5.00) compared to the pre-test (Mdn = 4.00). This improvement showed the largest effect size (*r*<sub>rb</sub> = |.95|) observed in this study. The
postvocalic /r/ retained a consistently high median of 5.00 both before and after the intervention. Despite this consistency, the Wilcoxon signed-rank test revealed a statistically significant change, Z = -3.89, *p* < .001, with a large effect size (r<sub>rb</sub> = |.69|), meaning that the pattern of changes in pronunciation accuracy indicates an overall improvement in the group, despite the median difference being 0.

**5. Discussion and key findings**

This study sought to explore advanced EFL learners’ development in pronunciation of the segmentals /ɒ – α:/, /əʊ – oʊ/, and postvocalic /r/ before and after 14 weeks of EPI. Overall, the results of this study indicate a considerable improvement in these features.

The observed improvements align with existing studies looking at other segmental features, which emphasise the positive impact of EPI (Bouchhioua, 2016; Saito, 2011). All target features showed statistically significant progress with varying effect sizes. While productions of the /ɒ – α:/ contrast exhibited a small effect size, close to moderate, productions of postvocalic /r/ reflected a large effect size. Most notably, the greatest improvement was found in the diphthong contrast /əʊ – oʊ/, with an effect size of r<sub>rb</sub> = |.95|. This is noteworthy given Tatzl’s (2011) observation that German-speaking learners struggle more with vowels and diphthongs than consonants, and Mende’s (2009) finding that they tend to monophthongise diphthongs. Our analysis shows that EPI courses like PPOCS1 can effectively address these issues.

Beyond reinforcing existing research on the efficacy of EPI for EFL learners’ pronunciation of selected segmental features, it is essential to consider the nature of the instruction provided. In line with Atar’s (2018) suggestion, focused practice in controlled speech contexts likely played a significant role in helping students internalise the respective features. Moreover, receiving regular feedback along with listening to weekly audio recordings (Galante & Piccardo, 2022) may have also contributed to the learners’ gains in their pronunciation. It is likely that the combination of some or all of these instructional elements collectively contributed to the students’ progress. Hence, by providing a space for both perception- and production-based practice, a foundation for effective instruction can be formed (Atar, 2018).

The results contribute to the broader field of pronunciation research by demonstrating that EPI can be effective for German-speaking EFL learners and highlight the importance of integrating pronunciation training into tertiary education settings. Another pedagogical implication of this study is the benefit of sustained pronunciation instruction. While notable gains were achieved within one semester, features with smaller effect sizes, such as /ɒ – α:/, may require longer-term practice for full mastery. Embedding ongoing EPI across multiple semesters could further support learners’ development (Saito, 2011). Overall, these findings highlight the value of making pronunciation instruction a continuous and integral part of language education at the tertiary level.

**6. Conclusion**

This study demonstrates that EPI significantly improved the pronunciation of the /ɒ – α:/, /əʊ – oʊ/, and postvocalic /r/ sounds among advanced EFL students at the University of Vienna. All targeted sounds showed statistically significant improvement, with the largest gains observed for the diphthongs /əʊ – oʊ/, which are identified as challenging for speakers of Austrian German. By and large, these results support other research on the effectiveness of EPI. However, the study has several limitations. First, it focused on read-aloud passages rather than spontaneous speech, which may not fully reflect students’ natural pronunciation use. Second, it cannot be ruled out that students' improvement was partially influenced by
exposure to English accents through other university courses or external sources beyond
explicit instruction. Third, using selected segmental features as the cornerstone of this study
gives only a partial view of students' pronunciation development, and the small number of
tokens for /ð – a:/ and /əʊ – oʊ/ may affect result reliability, potentially skewing κw values or
exaggerating effect sizes. Despite these limitations, the findings underscore the value of EPI
for German-speaking EFL learners. Future research may benefit from conducting delayed post-
tests to answer questions about long-term retention and employing a control group to determine
whether the observed outcomes are directly attributable to EPI.

Acknowledgements

We would like to thank Dr. Karin Richter for her support and guidance in the development of
this paper. Her help and expertise have been crucial, and without her, this project might never
have started. We would also like to express our sincere thanks to our colleague Anita, whose
support as a fourth rater was greatly appreciated.

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# Appendix

## Rating scale

/ð - α:/ (n = 6)

<table><thead><tr><th>Level</th><th>Approximated tokens</th><th>%</th></tr></thead><tbody><tr><td>Not approximated target variety (1)</td><td>[0;1]</td><td>0-20</td></tr><tr><td>Slightly approximated target variety (2)</td><td>[2]</td><td>21-40</td></tr><tr><td>Moderately approximated target variety (3)</td><td>[3]</td><td>41-60</td></tr><tr><td>Mostly approximated target variety (4)</td><td>[4]</td><td>61-80</td></tr><tr><td>Fully approximated target variety (5)</td><td>[5;6]</td><td>81-100</td></tr></tbody></table>

/əʊ - oʊ/ (n = 7)

<table><thead><tr><th>Level</th><th>Approximated tokens</th><th>%</th></tr></thead><tbody><tr><td>Not approximated target variety (1)</td><td>[0;1]</td><td>0-20</td></tr><tr><td>Slightly approximated target variety (2)</td><td>[2]</td><td>21-40</td></tr><tr><td>Moderately approximated target variety (3)</td><td>[3;4]</td><td>41-60</td></tr><tr><td>Mostly approximated target variety (4)</td><td>[4;5]</td><td>61-80</td></tr><tr><td>Fully approximated target variety (5)</td><td>[6;7]</td><td>81-100</td></tr></tbody></table>

postvocalic /r/ (n = 20)

<table><thead><tr><th>Level</th><th>Approximated tokens</th><th>%</th></tr></thead><tbody><tr><td>Not approximated target variety (1)</td><td>[0;4]</td><td>0-20</td></tr><tr><td>Slightly approximated target variety (2)</td><td>[5;8]</td><td>21-40</td></tr><tr><td>Moderately approximated target variety (3)</td><td>[9;12]</td><td>41-60</td></tr><tr><td>Mostly approximated target variety (4)</td><td>[13;16]</td><td>61-80</td></tr><tr><td>Fully approximated target variety (5)</td><td>[17;20]</td><td>81-100</td></tr></tbody></table>
**About the authors**

**Valerie White-Hautzinger** recently completed her Master of Education in English, Psychology, and Philosophy at the University of Vienna, Austria. As a pronunciation tutor for General American at the University of Vienna, she has developed a strong research focus on pronunciation instruction and acquisition. This focus is also central to her master's thesis, which explores the effects of explicit instruction on features L1 German speakers find particularly challenging when acquiring the General American accent. In addition to her work on pronunciation, Valerie has developed a growing interest in bilingualism, which she aims to explore further in her future research.

Email: valerie.hautzinger@univie.ac.at

**Marija Djenadic** is currently finishing her Master of Education studies in English, Psychology, and Philosophy at the University of Vienna, Austria. Due to her role as a British English pronunciation tutor at the University of Vienna, her research focuses on the teaching and learning of English pronunciation to EFL learners. As part of her current Master Thesis project, she is conducting a longitudinal study investigating the long-term effects of explicit pronunciation instruction. Her further research interests revolve around various aspects of foreign language learning, particularly concerning the connection between musicality and phonetic language aptitude, drawing on her own musical background.

Email: marija.djenadic@univie.ac.at

**Carolin Rumpler** obtained a bachelor's degree in English and American Studies and a Bachelor of Education in English and German from the University of Vienna, Austria. She is currently pursuing a Master of Education and a Master's in Anglophone Literatures and Cultures. As a pronunciation tutor for British English at the University of Vienna, she has grown particularly interested in the effects of pronunciation instruction. Her other research interests include historical linguistics and corpus linguistics, which she previously explored as a research assistant on the project *Relational Adjectives in the History of German* (RAHiG) at the Austrian Academy of Sciences (ÖAW).

Email: carolin.rumpler@univie.ac.at

**Miriam Fiala** is pursuing a Master's degree in Anglophone Literatures and Cultures at the University of Vienna, where she also works as an organisational assistant for the research platform GAIN. As a tutor in English pronunciation, she conducted research focusing on speech perception and retention. Her current areas of interest include Gender and Queer Studies, archival research, and literary and cultural analyses of the Victorian era, as well as the works of Aphra Behn and Friederike Mayröcker. In her Master's thesis, she examines how femslash fanfiction, using *Resident Evil* as a case study, subverts patriarchal structures within video game cultures.

Email: miriam.fiala@univie.ac.at
Index

<table><tr><td>accent</td><td>vi, vii, xiii, 17, 19, 21, 22, 23, 40, 46, 60, 61, 84, 85, 112, 119, 125, 130</td></tr><tr><td>accentedness</td><td>vi, vii, xi, 70, 73, 92</td></tr><tr><td>accents</td><td>xii, 22, 34, 41, 60, 119, 125</td></tr><tr><td>accommodation</td><td>111</td></tr><tr><td>accuracy</td><td>ix, x, 43, 44, 46, 47, 48, 52, 56, 61, 84, 85, 124</td></tr><tr><td>action research</td><td>55, 56, 64, 65</td></tr><tr><td>affective</td><td>3, 61</td></tr><tr><td>age</td><td>17, 31, 41, 42, 43, 60, 62, 73, 93, 100, 112</td></tr><tr><td>AI</td><td>64</td></tr><tr><td>American</td><td>viii, 17, 34, 41, 72, 75, 77, 99, 100, 118, 119, 120</td></tr><tr><td>applied research</td><td>55, 65</td></tr><tr><td>Arabic</td><td>viii, ix, 119, 120</td></tr><tr><td>ASR</td><td>64</td></tr><tr><td>assessment</td><td>ix, x, 1, 2, 29, 59, 61, 92, 93</td></tr><tr><td>attitude(s)</td><td>17, 31, 32, 42, 43, 62, 66, 70, 110</td></tr><tr><td>auditory analysis</td><td>118, 122</td></tr><tr><td>Austrian</td><td>viii, ix, 97, 118, 125</td></tr><tr><td>authentic</td><td>11, 15, 16, 17, 22, 23, 24, 54, 56, 58, 59, 62, 63, 64, 65</td></tr><tr><td>authentic speech</td><td>11, 16, 17, 22, 23</td></tr><tr><td>awareness</td><td>15, 16, 22, 28, 53, 63, 81, 109, 110, 114, 115</td></tr><tr><td>British</td><td>viii, 17, 29, 34, 41, 72, 75, 118, 119</td></tr><tr><td>Catalan</td><td>viii, 43, 45, 63</td></tr><tr><td>chatbots</td><td>64</td></tr><tr><td>ChatGPT</td><td>63</td></tr><tr><td>Chinese</td><td>viii, 16</td></tr><tr><td>CLT</td><td>59</td></tr><tr><td>clusters</td><td>3, 112, 113, 114</td></tr><tr><td>cognitive load</td><td>59</td></tr><tr><td>competence</td><td>1, 2, 10, 17, 30, 97, 98, 99, 100, 105, 106</td></tr><tr><td>computer-generated visuals</td><td>64</td></tr><tr><td>confidence</td><td>x, 28, 30, 34, 35, 37, 40, 65, 97, 98, 99, 102, 105, 106</td></tr><tr><td>connected speech</td><td>x, 3, 5, 10, 15, 16, 17, 18, 19, 20, 22, 23, 44, 52, 113, 114</td></tr><tr><td>consonants</td><td>33, 34, 85, 86, 112, 113, 114, 119, 124</td></tr></table>

<table><tr><td>Croatia</td><td>viii, 120</td></tr><tr><td>CSP</td><td>15, 16</td></tr><tr><td>Czech</td><td>viii, 120</td></tr><tr><td>Danish</td><td>viii, ix, 84, 85, 86, 87, 88, 92, 93, 94</td></tr><tr><td>delivery</td><td>3, 16, 22, 58, 60, 61, 65</td></tr><tr><td>diphthongs</td><td>113, 114, 119, 124</td></tr><tr><td>discourse-level</td><td>57, 61</td></tr><tr><td>Dutch</td><td>viii, 120</td></tr><tr><td>effectiveness</td><td>x, 11, 40, 56, 57, 58, 59, 61, 65, 98, 105, 124</td></tr><tr><td>EFL learners</td><td>15, 16, 17, 22, 23, 85, 86, 119, 120, 124, 125</td></tr><tr><td>EFL students</td><td>118, 119, 124</td></tr><tr><td>elision</td><td>x, 4, 5, 9, 10, 15, 17, 18, 19, 21, 22, 23, 114</td></tr><tr><td>EPI</td><td>118, 119, 120, 124</td></tr><tr><td>error(s)</td><td>xi, 16, 30, 33, 50, 56, 62, 65, 122</td></tr><tr><td>evaluation(s)</td><td>3, 65, 84, 85, 86, 88, 89, 91, 92, 93, 94, 105</td></tr><tr><td>explicit pronunciation teaching</td><td>30, 98, 103</td></tr><tr><td>facial expression</td><td>60</td></tr><tr><td>feedback</td><td>vi, vii, xi, 30, 32, 36, 57, 62, 87, 102, 105, 119, 120, 122, 124</td></tr><tr><td>films</td><td>59, 64</td></tr><tr><td>fluency</td><td>ix, xi, 22, 23, 43, 44, 46, 47, 48, 49, 52, 61, 64, 92, 119</td></tr><tr><td>French</td><td>viii, 16, 60</td></tr><tr><td>fricative(s)</td><td>x, 74, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93</td></tr><tr><td>dental</td><td>28, 33, 34, 36, 37, 62, 115</td></tr><tr><td>General American English</td><td>75, 118, 119</td></tr><tr><td>generalisability</td><td>57</td></tr><tr><td>German</td><td>viii, 119, 120, 121, 124, 125</td></tr><tr><td>gesture(s)</td><td>vi, vii, ix, xi, 60, 61</td></tr><tr><td>Global English</td><td>17, 22, 117</td></tr><tr><td>Golden Speaker</td><td>64</td></tr><tr><td>Haussa</td><td>viii, ix</td></tr><tr><td>hesitation</td><td>2</td></tr><tr><td>Hungarian</td><td>viii, 28, 30, 31, 32, 33, 34, 36, 37, 40, 41, 42, 120</td></tr><tr><td>HVPT</td><td>56, 57, 58, 59, 85</td></tr><tr><td>identity</td><td>60, 61, 62, 111</td></tr><tr><td>Indian</td><td>viii, ix</td></tr></table>
<table>
  <tr>
    <td>intelligibility</td>
    <td>vi, vii, ix, x, xi, 3, 16, 17,<br>29, 56, 57, 58, 61, 62, 65,<br>71, 73, 78, 80, 81, 84, 89,<br>90, 91, 93, 98, 111, 114,<br>118, 119</td>
  </tr>
  <tr>
    <td>intervention</td>
    <td>x, 17, 65, 110, 123, 124</td>
  </tr>
  <tr>
    <td>intonation</td>
    <td>xiii, 42, 44, 50, 52, 56, 57,<br>60, 61, 64, 113, 114, 115</td>
  </tr>
  <tr>
    <td>intrusive</td>
    <td>5, 7, 9, 10, 15, 17, 18, 19,<br>21, 22, 23</td>
  </tr>
  <tr>
    <td>IPA</td>
    <td>33, 35, 36, 37, 102</td>
  </tr>
  <tr>
    <td>Italian</td>
    <td>viii</td>
  </tr>
  <tr>
    <td>Japanese</td>
    <td>viii, 58, 85, 119</td>
  </tr>
  <tr>
    <td>L2</td>
    <td>1, 2, 3, 4, 11, 16, 17, 44, 53,<br>57, 60, 61, 62, 63, 64, 72,<br>73, 84, 85, 92, 93, 94, 105,<br>106, 119</td>
  </tr>
</table>

<table>
  <tr>
    <td>language acquisition</td>
    <td>109, 110, 111</td>
  </tr>
  <tr>
    <td>attitudes</td>
    <td>110, 111</td>
  </tr>
  <tr>
    <td>lab</td>
    <td>99, 118, 120, 121, 122</td>
  </tr>
  <tr>
    <td>listen and repeat</td>
    <td>33, 35, 36</td>
  </tr>
  <tr>
    <td>listener(s)</td>
    <td>vii, xiii, 2, 3, 30, 44, 46, 52,<br>62, 63, 73, 74, 78, 80, 84,<br>85, 86, 88, 89, 92, 93, 94,<br>115, 119, 122</td>
  </tr>
</table>

<table>
  <tr>
    <td>listening comprehension</td>
    <td>xi, 1, 2, 3, 4,<br>5, 11, 16, 17, 24, 41</td>
  </tr>
  <tr>
    <td>Macedonian mentor</td>
    <td>viii, ix, 15, 17, 18, 23, 24</td>
  </tr>
  <tr>
    <td>methodological factors</td>
    <td>113</td>
  </tr>
  <tr>
    <td>mirroring</td>
    <td>60, 61, 65</td>
  </tr>
  <tr>
    <td>mobile media</td>
    <td>59</td>
  </tr>
  <tr>
    <td>monosyllabic</td>
    <td>87, 115</td>
  </tr>
  <tr>
    <td>MOOC</td>
    <td>vii, ix, x, xi, xiii, 109, 110,<br>111, 113, 115</td>
  </tr>
  <tr>
    <td>movement</td>
    <td>33, 43, 61, 62</td>
  </tr>
  <tr>
    <td>native speech</td>
    <td>x, 16, 17, 18, 22, 24, 85</td>
  </tr>
  <tr>
    <td>naturalness</td>
    <td>xi, 15, 16, 17, 22, 23</td>
  </tr>
  <tr>
    <td>non-native</td>
    <td>2, 16, 17, 20, 23, 30, 85, 86,<br>88, 92, 93, 94</td>
  </tr>
  <tr>
    <td>Norwegian open access</td>
    <td>ix, 85</td>
  </tr>
  <tr>
    <td>orthographic outcomes</td>
    <td>i, v<br>ix, 64, 88<br>x, 43, 56, 57, 58, 66, 107,<br>125</td>
  </tr>
  <tr>
    <td>Pakistan pedagogy</td>
    <td>ix<br>x, 30, 58, 59, 60, 63, 65, 97,<br>98, 99, 104, 105</td>
  </tr>
  <tr>
    <td>perception and production</td>
    <td>x, 22, 23, 84,</td>
  </tr>
</table>

<table>
  <tr>
    <td>perceptions</td>
    <td>x, xi, 15, 16, 17, 22, 23, 44,<br>99, 100, 105</td>
  </tr>
  <tr>
    <td>perceptual training</td>
    <td>x, 84, 85, 86, 88, 92,<br>94</td>
  </tr>
  <tr>
    <td>Philippines</td>
    <td>viii</td>
  </tr>
  <tr>
    <td>phonetic training</td>
    <td>56, 84</td>
  </tr>
  <tr>
    <td>pitch</td>
    <td>43, 44, 46, 50, 52, 53, 58,<br>61, 62, 68, 114</td>
  </tr>
  <tr>
    <td>Polish</td>
    <td>viii, 1, 2, 120</td>
  </tr>
  <tr>
    <td>practical phonetics</td>
    <td>99, 105, 118, 119</td>
  </tr>
  <tr>
    <td>proficiency</td>
    <td>xi, 4, 9, 44, 52, 53, 60, 62,<br>73, 93, 97, 105, 106, 119,<br>120</td>
  </tr>
  <tr>
    <td>prominence</td>
    <td>3, 18, 61, 109, 114</td>
  </tr>
  <tr>
    <td>prosodic</td>
    <td>44, 61, 62, 64</td>
  </tr>
  <tr>
    <td>prosody</td>
    <td>vii, ix, x, 43, 44, 46, 49, 52,<br>59, 61, 62, 64</td>
  </tr>
  <tr>
    <td>quantitative</td>
    <td>ix, 18, 32, 68, 71, 76, 97,<br>100</td>
  </tr>
  <tr>
    <td>rating scale</td>
    <td>118, 121, 122</td>
  </tr>
  <tr>
    <td>reading aloud</td>
    <td>46, 48, 119, 121</td>
  </tr>
  <tr>
    <td>reliability</td>
    <td>32, 57, 88, 122, 125</td>
  </tr>
  <tr>
    <td>replication</td>
    <td>56, 57, 58, 64</td>
  </tr>
  <tr>
    <td>research base</td>
    <td>55, 56, 64</td>
  </tr>
  <tr>
    <td>research design</td>
    <td>ix, 56, 57</td>
  </tr>
  <tr>
    <td>rhythm</td>
    <td>x, 16, 19, 22, 23, 33, 34, 41,<br>44, 56, 61</td>
  </tr>
  <tr>
    <td>Romanian salience</td>
    <td>ix, 120<br>x, 109, 110, 111, 112, 113,<br>114, 115</td>
  </tr>
  <tr>
    <td>Scottish scripted texts</td>
    <td>1, 2, 3, 9, 11</td>
  </tr>
  <tr>
    <td>secondary school</td>
    <td>ix, 28, 29, 31, 34, 35</td>
  </tr>
  <tr>
    <td>segmentals</td>
    <td>56, 62, 113, 120, 124</td>
  </tr>
  <tr>
    <td>shadowing</td>
    <td>vi, vii, ix, xi, 58, 59, 64</td>
  </tr>
  <tr>
    <td>sibilants</td>
    <td>84, 85, 86, 89, 93, 94</td>
  </tr>
  <tr>
    <td>silent letters</td>
    <td>28, 33, 34, 36, 41, 44</td>
  </tr>
  <tr>
    <td>Sindhi</td>
    <td>viii</td>
  </tr>
  <tr>
    <td>social meaning</td>
    <td>60, 61, 62, 111</td>
  </tr>
  <tr>
    <td>social value</td>
    <td>109, 115</td>
  </tr>
  <tr>
    <td>Spain</td>
    <td>iv, viii, ix, 44</td>
  </tr>
  <tr>
    <td>Spanish</td>
    <td>viii, 30, 63, 71, 72, 73, 78,<br>80, 85</td>
  </tr>
  <tr>
    <td>speaking style</td>
    <td>3, 61, 65</td>
  </tr>
  <tr>
    <td>speech rate</td>
    <td>2, 3, 6, 8, 9, 15, 16, 18, 22,<br>44, 46, 52, 76, 84, 114</td>
  </tr>
  <tr>
    <td>spoken text(s)</td>
    <td>1, 2, 4, 5, 9, 11</td>
  </tr>
  <tr>
    <td>spontaneous texts</td>
    <td>9</td>
  </tr>
  <tr>
    <td>Standard British English</td>
    <td>118, 119</td>
  </tr>
</table>
<table>
  <tr>
    <td>stereotyped</td>
    <td>62, 111</td>
  </tr>
  <tr>
    <td>stigmatisation</td>
    <td>115</td>
  </tr>
  <tr>
    <td>story</td>
    <td>42, 43, 44, 45, 46, 48, 49, 52, 53, 121</td>
  </tr>
  <tr>
    <td>stress</td>
    <td>x, xi, 11, 31, 33, 34, 41, 42, 44, 45, 56, 57, 60, 61, 63, 64, 73, 112, 113, 114, 115</td>
  </tr>
  <tr>
    <td>word</td>
    <td>31, 34, 37, 41, 57, 112, 113, 114</td>
  </tr>
  <tr>
    <td>student teachers</td>
    <td>ix, 97, 99, 100, 102, 103, 104, 105, 106</td>
  </tr>
  <tr>
    <td>styling</td>
    <td>60</td>
  </tr>
  <tr>
    <td>suprasegmental(s)</td>
    <td>10, 44, 56, 57, 60, 61, 62, 64, 65, 99, 109, 113, 114, 115, 118, 119, 120</td>
  </tr>
  <tr>
    <td>Swedish</td>
    <td>viii, 85</td>
  </tr>
  <tr>
    <td>target</td>
    <td>vii, x, xi, 4, 21, 30, 37, 40, 57, 81, 87, 88, 90, 92, 93, 110, 118, 119, 120, 122, 123, 124, 127</td>
  </tr>
  <tr>
    <td>task-based</td>
    <td>59</td>
  </tr>
  <tr>
    <td>teacher education</td>
    <td>x, 29, 37, 97, 98, 99, 100, 105</td>
  </tr>
  <tr>
    <td>technology</td>
    <td>60, 63, 64, 65</td>
  </tr>
  <tr>
    <td>television</td>
    <td>59, 64</td>
  </tr>
  <tr>
    <td>tertiary</td>
    <td>118, 119, 124</td>
  </tr>
  <tr>
    <td>thought groups</td>
    <td>61</td>
  </tr>
  <tr>
    <td>timing</td>
    <td>61</td>
  </tr>
  <tr>
    <td>tonal</td>
    <td>114, 115</td>
  </tr>
  <tr>
    <td>transfer</td>
    <td>57, 60, 63</td>
  </tr>
  <tr>
    <td>Ukrainian</td>
    <td>viii, ix</td>
  </tr>
  <tr>
    <td>Urdu</td>
    <td>viii</td>
  </tr>
  <tr>
    <td>variationist</td>
    <td>109, 110</td>
  </tr>
  <tr>
    <td>variety</td>
    <td>x, 17, 29, 30, 56, 59, 64, 65, 111, 118, 120, 121, 122, 127</td>
  </tr>
  <tr>
    <td>video</td>
    <td>33, 42, 50, 63, 64</td>
  </tr>
  <tr>
    <td>Vietnamese</td>
    <td>viii, ix, 109, 110, 112, 113, 114, 115</td>
  </tr>
  <tr>
    <td>vowel(s)</td>
    <td>xi, 71, 73, 74, 75, 76, 77, 78, 79, 113, 115, 119, 124</td>
  </tr>
  <tr>
    <td>length</td>
    <td>71, 72, 73, 74, 76, 113, 114</td>
  </tr>
  <tr>
    <td>reduction</td>
    <td>17, 18, 19, 20, 22, 23, 25, 34</td>
  </tr>
  <tr>
    <td>voice quality</td>
    <td>50, 52, 61, 71, 76, 78, 81</td>
  </tr>
  <tr>
    <td>whole-body context</td>
    <td>60</td>
  </tr>
  <tr>
    <td>YouGlish</td>
    <td>64</td>
  </tr>
  <tr>
    <td>YouTube</td>
    <td>64</td>
  </tr>
</table>