consonant cluster does not exist in French, suggesting that the effects of orthography can override L1 phonotactic restrictions. Bassetti et al. (2018) had similar results, with geminate consonants produced by L1 Italian learners and late bilinguals in contexts where they would not be present in the L1. Additionally, we observed an interaction of orthographic and phonotactic effect in the reading aloud task, where many participants had the tendency to nasalise the preceding vowel in *lamb* and *comb* leading to non-target-like realisations such as */'lʌb/ (12%) and */'kɔ̃b/ (43%). This effect was barely noticeable in the repetition task (1% for the former vs. 8% for the latter), thereby suggesting that phonotactic constraints and orthography interact with one another, resulting in the nasalisation of the vowel (since <–am-> and <–om-> are realised as /ə/ and /ɔ/ in French) and the realisation of an intrusive consonant prompted by the silent letter.

**5 Conclusion and perspectives**

To the best of our knowledge, our study is the first to analyse the effects of English silent letters on French learners of English. The results replicated those found by the original study (Bassetti & Atkinson, 2015): orthographic forms can affect production, even for experienced learners, all of whom produced at least some intrusive consonants for silent letters. Particularly, the effect was stronger in the reading aloud task than in the word repetition task, as in the original study. This confirms that providing orthographic input to learners can increase orthographic effects, even in a language with a phonologically opaque orthography.

From a teaching perspective, one solution involves withholding written input. Winitz and Yanes (2002) suggested that at the early ages of acquisition of second language learning, L2 learners should not be provided with written input until they acquire a correct pronunciation of these words; yet, preventing access to written material in an L2 is obviously very impractical for many reasons, if at all possible. Thornbury (1999, p. 86) attempted to delay students' exposure to the orthographic forms until the correct pronunciation of silent-letter words was completely acquired; however, results were not conclusive, as learners tended to revert to orthographic input once exposed to it.

However, it is important to stress that orthography should not be banished from L2 learning. Another way to minimise the influence of spelling-pronunciation habits is to encourage the learning of phonetic notation (Mompeán & Fouz-González, 2021). Not only can phonetic symbols provide all learners with the correct pronunciation of vocabulary, but as they are based on visual displays, they can also facilitate learners' categorisation, and conceptualisation of mental representations for target sounds – and might even correct fossilised mispronunciations.

An extension of this study (Mouquet & Mairano, 2023) will expand the limits of the present study, which in itself does not address all the effects of silent letters. Particularly, the limited number of target words, the limited variety of silent letters (five items for <b> and merely two for <d> and <l>), as well as the limited number of contexts (three items for <–mb>, one for <–lb>, one for <–lk>, one for <–lm>, and one for <–dn–>) do not give us a full picture. Therefore our findings do not provide an adequate basis for drawing generalisable conclusions. This study will therefore be expanded through an alternative design incorporating a greater number of letters, balanced contexts, and a larger set of items. Additionally, by conducting two tasks, a picture naming task and an ABX task in which a phoneme was added or deleted to the recording of each stimulus (e.g., */'sælmən/ for *salmon*) via Mbrola resynthesis, direct orthographic exposure may be discarded. This will be the opportunity to investigate whether L2 orthography, and more particularly silent letters, is entrenched in memory. Future research will attempt to provide further evidence for orthographic effects on L2 phonology.
## References

Aro, M. (2013). Learning to read: The effect of orthography. In R. Malatesha Joshi, & P. G., Aaron (Eds.), *Handbook of orthography and literacy* (pp. 545–564). Routledge.

Bassetti, B. (2008). Orthographic input and second language phonology. In T. Piske & M. Young-Scholten (Eds.), *Input matters in SLA* (pp. 191–206). https://doi.org/10.21832/9781847691118-013

Bassetti, B., Sokolovic-Perovic, M., Mairano, P., & Cerni, T. (2018) Orthography-induced length contrasts in the second language phonological systems of L2 speakers of English: Evidence from minimal pairs. *Language and Speech, 61*(4), 577–598. https://doi.org/10.1177/0023830918780141

Bassetti, B., & Atkinson, N. (2015). Effects of orthographic forms on pronunciation in experienced instructed second language learners. *Applied Psycholinguistics, 36*(1), 67–91. https://doi.org/10.1017/S0142716414000435

Best, C. T. (1994). The emergence of native-language phonological influences in infants: A perceptual assimilation model. In J. C. Goodman & H. C. Nusbaum (Eds.), *The development of speech perception: The transition from speech sounds to spoken words* (pp. 167–224). MIT Press.

Boersma, P., & Weenink, D. (2019). *Praat: Doing phonetics by computer* [Computer program]. Version 6.0.49, from http://www.praat.org/

Bürki, A., Welby, P., Clément, M., & Spinelli, E. (2019). Orthography and second language word learning: Moving beyond “friend or foe?”. *The Journal of the Acoustical Society of America, 145*(4), EL265-EL271. https://doi.org/10.1121/1.5094923

Davies, M. (2004). British National Corpus. Oxford University Press. https://www.english-corpora.org/bnc/

Erdener, V. D., & Burnham, D. K. (2005). The role of audiovisual speech and orthographic information in nonnative speech production. *Language Learning, 55*(2), 191–228. https://doi.org/10.1111/j.0023-8333.2005.00303.x

Escudero, P., Hayes-Harb, R., & Mitterer, H. (2008). Novel second-language words and asymmetric lexical access. *Journal of Phonetics, 36*(2), 345–360. https://doi.org/10.1016/j.wocn.2007.11.002

Escudero, P., Simon, E., & Mulak, K. E. (2014). Learning words in a new language: Orthography doesn't always help. *Bilingualism: Language and Cognition, 17*(2), 384–395. https://doi.org/10.1017/S1366728913000436

Flege, J. E. (1995). Second language speech learning: Theory, findings and problems. In W. Strange (Ed.), *Speech perception and linguistic experience: Issues in cross-language research* (pp. 233–277). York Press.

Mompeán, J. A., & Fouz-González, J. (2021). Phonetic symbols in contemporary pronunciation instruction. *RELC Journal, 52*(1), 155–168. https://doi.org/10.1177/003368822094343

Mouquet, M. & Mairano, P. (2023). Orthography and the mental lexicon: The effects of English silent letters on French learners. *Proc. of ICPhS 2023*, Prague (Czech Republic), 7-11 August 2023.

Seymour, P. H. K., Aro, M., & Erskine, J. M. (2003). Foundation literacy acquisition in European orthographies. *British Journal of Psychology, 94*(2), 143–174. https://doi.org/10.1348/000712603321661859

Thornbury, S. (1999). *How to teach grammar*. Longman Harlow.

Winitz, H., & Yanes, J. (2002). The development of first year, self-instructional university courses in Spanish and German. In P. Burmeister, T. Piske, & A. Pohde (Eds.), *An integrated view of Language development: Papers in honor of Henning Wode*. Wissenschaftlicher Verlag Trier.

## About the authors

Marine Mouquet is currently a PhD student at the University of Aix-Marseille, France, in the LPL research group (Laboratoire Parole et Langage, UMR 7309) under the supervision of Sophie Herment (LPL) and Paolo Mairano (STL). She received a Master's degree of English Linguistics and Didactics at the University of Lille, France during which she wrote two MA theses supervised by Paolo Mairano investigating the effects of L2 English silent letters on L1 French learners' pronunciation and perception and its impact on the mental lexicon. Her main
research interests lie in English phonetics and phonology, second language acquisition, the effects of orthography and psycholinguistics.

Email: marine.mouquet@univ-amu.fr

**Paolo Mairano** is currently Associate Professor in English Linguistics at the University of Lille, and has previously worked in various academic and industrial institutions (University of Warwick, University of Rouen, University of Turin, Nuance Inc.). His research focus lies in the acquisition of L2 sounds as well as other areas of phonetics, and his studies adopt a quantitative approach.

Email: paolo.mairano@univ-lille.fr
# Lyrics as a means of raising phonetic awareness: Transcription, pronunciation, and descriptive phonetics combined

Marta Nowacka
University of Rzeszow

The present study assessed an intervention aimed at increasing phonetic awareness among 95 university English non-majors through self-selected songs. It was primarily designed to boost the students' interest and raise the attractiveness of a course of descriptive phonetics (Nowacka, 2022). The focus is twofold: to show how the students performed the tasks and to exhibit how useful they found the approach. Data collection consisted of three consecutive tasks: 1) transcription of lyrics used for reading aloud; 2) analysis of the occurrence of 51 phonetic and morphophonemic features; and 3) the students' evaluation of the attractiveness and usefulness of the activity to improve their understanding of phonetic theory.

The results show that the identification of some features was difficult. Among the issues which require further classroom intervention are: /ŋ/ followed by /g/, aspiration, <-s> forms pronounced as /-ɪz/, KIT vowel, spread vowels, centring diphthongs, and schwa spelt <o>, <u>, and <a>. Nonetheless, the respondents found this activity (extremely) attractive (71%), (extremely) useful (76%) and more attractive than other phonetic tasks (49%). It was praised for the autonomy it offered in the choice of a text/song for analysis and for the practical application of phonetics.

**Keywords:** lyrics, phonetic awareness, descriptive phonetics, transcription

<figure><img src="image_69.png" /></figure>
# 1 Introduction

Songs have been recognised as a teaching tool in EFL for the enhancement of various skills, including pronunciation (Barrett, 2015; Hancock, 1999; Murphy, 2013; Tegge, 2018; Walker, 2006). Research on the use of songs for the improvement of pronunciation shows that singing-based intervention and focus on form positively affect pronunciation in general (Wilcox, 1995), as well as the enunciation of words (Baills et al., 2021; Saldiraner & Cinkara, 2021). The articulation of vowels has been shown to improve (Good et al., 2015) and also consonants, for example: consonant clusters by Japanese speakers (Nakataa & Shockey, 2011); final /k, g, t, d/, /l, r/ and /s, z/ by Thai speakers (Kanlayanee, 2012); dental and palato-alveolar fricatives by Indonesians (Aini et al., 2013; Stefani et al., 2015); and /θ/ and /ŋ/ by Czechs (Wodecki, 2014). Improvements in some connected speech phenomena have also been documented, such as linking by Czechs (Wodecki, 2014), as well as in relation to prosody (Degrave, 2021), fluency and naturalness of Iranians (Ashtian & Zafarghandi, 2015), aspiration and fluency of reading by Italians (Tizian, 2016), and new word segmentation by French speakers (Schön et al., 2008).

The present paper focuses on the use of transcribed lyrics in order to revise English phonetics, which, to the author's best knowledge has not yet been a target of phonetic research. A pragmatic intention behind the design of this study was to revise selected features of English phonetics by means of an out-of-class assignment at the end of the first semester, after studying segmental phonetics. The aim was to more actively engage the first-year university English students in self-study and to show them that their understanding of English and recognition of a speaker's accent could benefit from a detailed phonetic analysis of transcribed lyrics of their own choice. It was also an attempt to make the compulsory course of descriptive phonetics more entertaining, because it was perceived as being boring and useless in practical life (Nowacka, 2022).

# 2 Research methodology

## 2.1 Research questions

The study aims to answer two questions: the first regarding the students' performance of the task and the second concerning its attractiveness and usefulness.

**RQ1:** What phonetic features do the students identify correctly in their transcriptions of self-selected lyrics and what features are they still unable to exemplify at the end of the first semester of segmental phonetics?

**RQ2:** How attractive and useful do the participants find the approach?

## 2.2 Participants

The respondents were 95 first-year (19–20 years old; M = 29, F = 66) English students of the University of Rzeszow, Poland, at the end of a compulsory 45-hour phonetic training on segmental phonetics. The training included 15 hours of lectures and a 30-hour practical pronunciation course. They had no prior knowledge of suprasegmental phonetics, which started in the second term. All participants were coded S.1–S.95 for anonymity.
## 2.3 Instruments

The data was collected by means of three different tasks which were part of the obligatory, one-semester, English segmental phonetics and pronunciation course. The tasks were performed on the Microsoft Teams platform. They included:

1. Task 1 – transcription of the lyrics of a self-selected song.

2. Task 2 – a questionnaire used to analyse the occurrence of 51 phonetic and morphophonemic features in the lyrics text.

3. Task 3 – a questionnaire used to evaluate the attractiveness and usefulness of the activity using a 5-point Likert scale (1 = not attractive/useful at all; 2 = not very attractive/useful; 3 = neither attractive/useful nor unattractive/useless; 4 = attractive/useful; 5 = extremely attractive/useful). The questionnaire also included open questions:

- What did you learn from this activity? Why was it useful?

- Did you find this activity more attractive than other phonetic activities? Why?

- What was attractive about the activity?

- What did you not like about this activity?

## 2.4 Procedure

Task 1 was conducted first. The students were asked to prepare their transcriptions but were allowed to consult online applications, such as Photransedit<fnref n="1" /> or others. Then, in the classroom these transcriptions were used as a warm-up exercise in reading aloud from transcription.

The core part of the study (Task 2) focused on students' ability to identify words with selected vowels, consonants, connected speech phenomena, inflected and derived forms, letter-to-sound correspondences, proper names, non-grammatical forms, as well as non-standard and accent-specific variants (see Table 1). The scope of the analysis was wide, consisting of 51 phonetic and morphophonemic features. As a revision the students were asked to analyse their transcribed texts, correct them if necessary, and to analyse them by searching for these 51 pronunciation features. This task was carried out to put the students' theoretical knowledge into practice.

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

*Pronunciation Features Analysed in Students' Transcriptions*

<table><thead><tr><td>Feature type</td><td>Pronunciation feature</td></tr></thead><tbody><tr><td>Vocalic features</td><td>- five pure vowels: FLEECE, KIT, NURSE, TRAP, LOT<sup>2</sup><br/>- schwa<sup>3</sup> spelt as &lt;a&gt;, &lt;o&gt; and &lt;u&gt;</td></tr></tbody></table>

<footnote n="1">¹ Photransedit is available at http://www.photransedit.com</footnote>

<footnote n="2">² These are Wells' (1982, p. 120) standard lexical sets for English vowels: FLEECE: /i:/, KIT: /ɪ/, NURSE: /ɜ:/, TRAP: /æ/, and LOT: /ɒ/. They are used as keywords in the main text.</footnote>

<footnote n="3">³ The keyword commA: /ə/ (Wells, 1982, p. 120) has been replaced by the more widely known term schwa, which is used in the main text.</footnote>
<table><thead><tr><th>Feature type</th><th>Pronunciation feature</th></tr></thead><tbody><tr><td>Consonantal features</td><td><ul><li>centring diphthongs or a corresponding monophthong /-ir, -er, -or/</li><li>tripthongs (e.g., nowadays as /'naʊədeɪz/) or a smoothed version of (e.g., /'naʊdeɪz/)</li><li>three types of vowels: spread, central, and low</li><li>final voiced obstruents</li><li>aspiration</li><li>rhoticity/non-rhoticity</li><li>dental fricatives /θ/ and /ð/</li><li>/ɪ/ followed and not followed by /g/</li><li>dark [h]</li></ul></td></tr><tr><td>Connected speech features</td><td><ul><li>contracted, weak, and strong forms</li><li>linking and/or intrusive /r/</li><li>the article <i>the</i> and the preposition <i>to</i> before a vowel and a consonant</li></ul></td></tr><tr><td>Inflected and derived forms</td><td><ul><li>plurals of nouns, possessives, 3rd person singular present tense &lt;-s&gt; forms pronounced as /s/, /z/, /ɪz/</li><li>past tenses and past participles &lt;-ed&gt; forms pronounced as /t/, /d/, /ɪd/</li><li>present participles &lt;-ing&gt; forms pronounced as /ɪŋ/ or /ɪn/</li></ul></td></tr><tr><td>Spelling-to-pronunciation patterns</td><td><ul><li>the letter &lt;i&gt; as KIT vowel</li><li>&lt;a&gt; before a consonant as TRAP vowel</li><li>&lt;ea&gt; or &lt;ee&gt; as FLEECE vowel</li><li>&lt;(w)or&gt; as NURSE vowel</li><li>&lt;oo&gt; as GOOSE vowel</li><li>&lt;ar&gt; as START vowel</li><li>&lt;or&gt; as STRUT or NURSE vowels</li><li>a suffix &lt;-ate&gt; as /-ət/ in nouns or adjectives or /-eɪt/ in verbs</li><li>a suffix &lt;-ous&gt; as /-əs/</li></ul></td></tr><tr><td>Other</td><td><ul><li>proper names</li><li>non-grammatical forms</li><li>non-standard and accent-specific pronunciation</li></ul></td></tr></tbody></table>

In the follow-up part (Task 3) conducted at the end of the course, the students were required to evaluate how attractive and useful the task was for learning and understanding English phonetics and in comparison with other phonetic activities.

Respondents were also encouraged to provide from one to three words corresponding to a given feature, and for data analysis these were categorised into four types: right, wrong, both right and wrong, and not applicable and/or not provided. For example, the answers regarding &lt;-ed&gt; forms of regular verbs pronounced as /d/ were classified as one of the following options:

* right – e.g., discovered /dɪ'skʌvəd/, ruled /ru:ld/, begged /begd/;

* wrong – e.g., begged /'beged/, bored /'bɔ:red/.

The ‘right and wrong’ category was used when students gave both in a single answer, e.g., bored /bɔ:d/ and /'bɔ:red/.
# 3 Data analysis and results

## 3.1 Songs chosen by students

Regarding the choice of a singer or band and their accent, 95 respondents opted for mainly British or American artists. There is a slight preference for British (n = 49) over American (n = 40) English. Among the remaining six replies respondents listed two Australians and Canadians, one singer with German English, and one with Korean English.

Altogether 86 different lyrics were chosen by 95 respondents, with Adele's (n = 8) and Ed Sheeran's (n = 5) songs the most frequently selected (see Appendix). Adele's lyrics included: Set Fire to the Rain (n = 3), Hello (n = 2), Someone Like You (n = 1), When We Were Young (n = 1), and Chasing Pavements (n = 1). Ed Sheeran's list included: Thinking out Loud (n = 2), Bad Habits (n = 1), Castle on the Hill (n = 1), and I See Fire (n = 1).

## 3.2 Results

In general, the results are optimistic. Six features, marked in grey in Table 2, constituted over 90% of correct answers, for instance, FLEECE (98%), LOT (98%), TRAP (97%), weak forms (95%), voiced dental fricatives (93%), and contractions (92%). This suggests that the vast majority of the respondents had no difficulty in exemplifying these terms. In order to emphasise those features which require further practice in the classroom, the results in Table 2 are arranged in ascending order of wrong answers.

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

*Percentage of Responses for Each Pronunciation Feature (All Data)*<fnref n="4" />

<table><thead><tr><th>Pronunciation feature</th><th>right %</th><th>wrong %</th><th>right and wrong %</th><th>not applicable / not provided %</th></tr></thead><tbody><tr><td>FLEECE</td><td>98%</td><td>0%</td><td>2%</td><td>0%</td></tr><tr><td>NURSE</td><td>75%</td><td>0%</td><td>6%</td><td>19%</td></tr><tr><td>/ɪ/ not followed by /g/</td><td>80%</td><td>0%</td><td>0%</td><td>20%</td></tr><tr><td>&lt;ar&gt; = START</td><td>57%</td><td>0%</td><td>0%</td><td>43%</td></tr><tr><td>&lt;-ous&gt; as /əs/</td><td>13%</td><td>0%</td><td>0%</td><td>87%</td></tr><tr><td>KIT</td><td>59%</td><td>1%</td><td>40%</td><td>0%</td></tr><tr><td>TRAP</td><td>97%</td><td>1%</td><td>1%</td><td>1%</td></tr><tr><td>LOT</td><td>98%</td><td>1%</td><td>0%</td><td>1%</td></tr><tr><td>to before C</td><td>77%</td><td>1%</td><td>3%</td><td>19%</td></tr><tr><td>contractions</td><td>92%</td><td>1%</td><td>0%</td><td>7%</td></tr><tr><td>&lt;-s&gt; forms as /z/</td><td>73%</td><td>1%</td><td>3%</td><td>23%</td></tr><tr><td>&lt;-ing&gt; forms as /ɪŋ/</td><td>80%</td><td>1%</td><td>7%</td><td>12%</td></tr><tr><td>voiced dental fricatives</td><td>93%</td><td>2%</td><td>4%</td><td>1%</td></tr><tr><td>weak forms</td><td>95%</td><td>2%</td><td>0%</td><td>3%</td></tr><tr><td>&lt;-ed&gt; forms of reg. v. as /t/</td><td>32%</td><td>2%</td><td>1%</td><td>65%</td></tr><tr><td>&lt;a&gt; = TRAP</td><td>86%</td><td>2%</td><td>0%</td><td>12%</td></tr><tr><td>&lt;ea&gt;, &lt;ee&gt; = FLEECE</td><td>86%</td><td>2%</td><td>0%</td><td>12%</td></tr><tr><td>tripthongs</td><td>33%</td><td>3%</td><td>1%</td><td>63%</td></tr></tbody></table>

<footnote n="4">⁴ Percentages may not total 100 due to rounding.</footnote>
<table><thead><tr><th>Pronunciation feature</th><th>right %</th><th>wrong %</th><th>right and wrong %</th><th>not applicable / not provided %</th></tr></thead><tbody><tr><td>voiceless dental fricatives</td><td>87</td><td>3</td><td>3</td><td>6</td></tr><tr><td>the before V</td><td>33</td><td>3</td><td>0</td><td>64</td></tr><tr><td>&lt;-s&gt; forms as /s/</td><td>55</td><td>3</td><td>0</td><td>42</td></tr><tr><td>&lt;i&gt; = KIT</td><td>89</td><td>3</td><td>4</td><td>3</td></tr><tr><td>dark l</td><td>71</td><td>4</td><td>4</td><td>21</td></tr><tr><td>&lt;or&gt; = STRUT, NURSE</td><td>26</td><td>4</td><td>2</td><td>67</td></tr><tr><td>centring diphthongs</td><td>59</td><td>5</td><td>22</td><td>14</td></tr><tr><td>spread V</td><td>65</td><td>5</td><td>25</td><td>4</td></tr><tr><td>final voiced C</td><td>78</td><td>5</td><td>6</td><td>11</td></tr><tr><td>&lt;-ing&gt; forms as /ɪn/</td><td>18</td><td>5</td><td>0</td><td>77</td></tr><tr><td>&lt;(w)or&gt; = NURSE</td><td>28</td><td>5</td><td>0</td><td>66</td></tr><tr><td>schwa as &lt;a&gt;</td><td>68</td><td>6</td><td>18</td><td>7</td></tr><tr><td>central V</td><td>80</td><td>6</td><td>12</td><td>2</td></tr><tr><td>the before C</td><td>88</td><td>6</td><td>0</td><td>5</td></tr><tr><td>to before V</td><td>15</td><td>6</td><td>3</td><td>76</td></tr><tr><td>&lt;oʊ&gt; = GOOSE</td><td>42</td><td>7</td><td>0</td><td>51</td></tr><tr><td>&lt;-ate&gt; as /ət/ (n./adj.), /eɪt/ (v.)</td><td>7</td><td>7</td><td>0</td><td>85</td></tr><tr><td>low V</td><td>76</td><td>9</td><td>12</td><td>3</td></tr><tr><td>linking r</td><td>38</td><td>9</td><td>0</td><td>53</td></tr><tr><td>rhoticity / non-rhoticity</td><td>78</td><td>11</td><td>0</td><td>12</td></tr><tr><td>strong forms</td><td>66</td><td>11</td><td>2</td><td>21</td></tr><tr><td>intrusive r</td><td>3</td><td>11</td><td>0</td><td>86</td></tr><tr><td>&lt;-ed&gt; forms of reg v. as /d/</td><td>39</td><td>11</td><td>3</td><td>47</td></tr><tr><td>&lt;-ed&gt; forms of reg v. as /ɪd/</td><td>23</td><td>11</td><td>2</td><td>64</td></tr><tr><td>schwa as &lt;o&gt;</td><td>53</td><td>16</td><td>16</td><td>16</td></tr><tr><td>aspiration</td><td>41</td><td>16</td><td>19</td><td>24</td></tr><tr><td>&lt;-s&gt; forms as /ɪz/</td><td>14</td><td>21</td><td>5</td><td>60</td></tr><tr><td>schwa as &lt;u&gt;</td><td>31</td><td>22</td><td>8</td><td>39</td></tr><tr><td>/ɪ/ followed by /g/</td><td>7</td><td>44</td><td>0</td><td>48</td></tr></tbody></table>

The results in the bottom lines of Table 2 reveal that one feature, /ɪ/ followed by /g/, was more confusing than all the others for the respondents, as it received 44% wrong responses and only 7% right ones (e.g., *single*). The respondents mistook the consonant for the letter and listed examples of words, in which /ɪ/ was not followed by /g/, however, in which the letter <g> was included in the spelling (e.g., *thinking*).

In addition, larger numbers of wrong answers were also found with reference to such features as: schwa spelt <u> (22%), 
 forms pronounced as /-ɪz/ (21%), aspiration (16%) and schwa spelt <o> (16%). When it comes to schwa spelt <u> (22%) (e.g., *support*, *success*, *suppose*), the most typical mistake which students made was to treat the sequence of letters <ou> as the letter <u> (e.g., the weak form of *could* /kəd/). Other less frequent incorrect examples included words with a letter <u> not pronounced as schwa, (e.g., *perfume*/'pɜ:fju:m/) or words in which schwa was found but it was represented by a letter different from <u> (e.g., with <o> in *tonight*). On the other hand, the most frequently given good examples comprised function words *just*, *but*, and *must*.

As regards the feature 
 forms pronounced as /ɪz/, only a small minority of the respondents correctly chose words in which the suffix 
-es> was pronounced as /ɪz/ after sibilants (14%). The majority of those who indicated this feature (26%) gave three types of
examples: words which ended with /ɪz/ regardless of their morphological structure, words in which the suffix <-s/-es> was pronounced as /z/ after voiced sounds (e.g., *eyes*), or words in which there was no suffix and where /ɪz/ was a part of a stem (e.g., *his*).

Aspiration created problems for 16% of the informants. Its erroneous cases included: no context for aspiration (e.g., *bones*), /pl-/, pr-/- and /st-/, sp-/- clusters (e.g., *plans*, *start*), and medial unstressed in unaspirated position of /p/ and /t/ (e.g., *sometimes*).

The analysis of erroneous responses concerning the feature schwa spelt <o> (16%) shows that respondents misinterpret schwa when it is a part of a GOAT diphthong (e.g., *so*, *hypo*) or a triphthong (e.g., *riot*). Among the good examples are both content words (e.g., *forget*) and functional words (e.g., *of*).

The next sub-sections (§3.2.1–3.2.5) present the results for each category of feature as listed in Table 1 (§2.4): vocalic features, consonantal features, connected speech features, inflected and derived forms, spelling-to-pronunciation patterns, and other features.

### 3.2.1 Responses for vocalic features

Figure 1 shows that for vowels, the percentage of ‘right’ answers outnumbers the combined ‘wrong’ and ‘right and wrong’ answers in all cases. Such features as FLEECE (98%), LOT (98%), TRAP (97%) and NURSE (75%), presented at the bottom of the graph, yielded a high percentage of correct examples (as shown by the green bar). Triphthongs (33%) were exemplified by one third of the group.

<caption>**Figure 1**</caption>

*The Percentage of Four Types of Responses for Vocalic Features*

<figure><img src="image_70.png" /></figure>

Schwa spelt <u> and <o>, discussed above, comprises 30% and 32% of erroneous and partly erroneous answers, respectively. The reason for a high number of ‘right and wrong’ responses for the KIT vowel (40%) is the faulty inclusion of words with closing diphthongs, such as FACE (e.g., *hate*), PRICE (e.g., *die*) and less frequently CHOICE (e.g., *boy*) vowels, as well
as an occasional centring diphthong NEAR (e.g., *we're*) as an erroneous example of a word with a sole KIT vowel.

The graph also illustrates that centring diphthongs (27%) and the terms referring to the tongue position or lip shape for vowels — low (21%), central (18%) and spread (30%) — have not been understood by all the respondents. The centring diphthongs were replaced by closing diphthongs (e.g., *now*). The three vowel categories (low, central, spread) were exemplified with a random vowel; for example, low vowels were misrepresented by mid vowels such as DRESS in *never* or NURSE in *word* or diphthongs in *my*. The problem with schwa spelt <a> (24%), similarly to the above-mentioned schwa spelt <o> and <u>, was that the respondents did not pay attention to the letter <a>. Moreover, even if they did (e.g., *around*, *another*), they also provided other words in which schwa was represented by a letter different from <a>, or they did not recognise that schwa was a part of a diphthong NEAR (e.g., *real*) and not a sound on its own.

### 3.2.2 Responses for consonantal features

For consonants, summarised in Figure 2, the results show that around three-quarters of the group correctly exemplified all the features, except for: 1) /ŋ/ followed by /g/, which resulted in 44% wrong answers versus 7% correct ones; and 2) aspiration, with a similar ratio of right (41%) to wrong and partially wrong answers (35%). Among the consonantal features are: /ð/ (93%) and /θ/ (87%) correct examples, /ŋ/ not followed by /g/ (80%), (non-)rhoticity (78%), a final voiced consonant (78%) and dark [h] (71%).

<caption>**Figure 2**</caption>

*The Percentage of Four Types of Responses for Consonants*

<figure><img src="image_71.png" /></figure>

As regards the identification of whether the variety is rhotic or non-rhotic, 11% of the informants gave an erroneous answer to this question, mentioning rhotic as well as non-rhotic transcriptions (e.g., *burns /bɜ:nz/* or /bɜ:rnz/), or words with the pre-vocalic /r/ (e.g., *cry*) which is common for both varieties. Interestingly, these results show that artists are using rhoticity
when singing, despite it not being a standard feature of their (expected) accent when speaking. Initially, when students categorised singers’ accents, 55% were classified as non-rhotic while 45% as rhotic. However, 70% of the respondents listed examples of non-rhotic pronunciations and 30% of rhotic ones, which contradict the findings of Trudgill (1997), wherein British pop and rock singers reportedly tend to adopt an Americanised singing style. Thus, the results on rhoticity suggest that roughly 15% of the students might still not understand what constitutes an example of rhoticity.

### 3.2.3 Responses for connected speech features

The predominantly green bars of Figure 3 show reassuring results for connected speech elements; right answers outnumber wrong ones except for intrusive /r/, for which there were only 3% correct instances and 11% wrong ones. A high percentage of right answers for weak forms (95%), contractions (92%), the article *the* before a consonant (88%), the preposition *to* before a consonant (77%) and strong forms (66%) implies that the respondents understood their function in English.

<caption>**Figure 3**</caption>

*The Percentage of Four Types of Responses for Connected Speech*

<figure><img src="image_72.png" /></figure>

One respondent whose lyrics did not include intrusive /r/, provided the following explanation “...you can actually hear the intrusive /r/ in some other Pink Floyd songs such as Astronomy Domine (e.g., *Oberon, Miranda_r_ and Titania*) or Corporal Clegg (e.g., *umbrella_r_ in the rain*)” (S.77). Wrong examples (11%) included: a) words with the right context for intrusive /r/, however, with no /r/ (*n* = 1) (e.g., *saw us /sɔ: ʌs/*); b) words with /r/ (*n* = 4) (e.g., *streets*); c) words with a silent letter <r> (*n* = 2) (e.g., *world /wɜ:ld/*); or d) an unrelated feature like linking (*n* = 3) (e.g., *away and*). As regards linking /r/, here 9% of the respondents gave wrong examples, which in fact exhibit rhoticity for American English (*n* =
4) (e.g., better /'betər/), non-rhoticity (n = 4) (e.g., before you /bɪ'fɔː/ ju/), or a wrong context for linking /r/, which is, namely, after a high vowel (n = 6) (e.g., you own, I am).

### 3.2.4 Responses for inflected and derived forms

The results for inflected and derived forms are presented in Figure 4. The only feature which scores low is <-s> forms pronounced as /ɪz/, discussed above under Table 2 (§3.2). In all other features, correct examples outnumber the incorrect ones, e.g., <-s> forms pronounced as: /z/ (73%) and /s/ (55%); <-ed> forms of verbs rendered as: /d/ (39%), /t/ (32%) and /ɪd/ (23%); and <-ing> forms articulated as: /ɪŋ/ (80%) or /ɪn/ (18%).

<caption>**Figure 4**</caption>

*The Percentage of Four Types of Responses for Inflected and Derived Forms*

<figure><img src="image_73.png" /></figure>

The pronunciation of <-s> forms as /z/ is a feature that is usually misarticulated by Polish respondents at the beginning of the course, owing to the lack of final voicing in the Polish language (Sobkowiak, 1996, p. 58). It is thus interesting to see the high percentage of right responses (73%). The incorrect responses for <-ed> forms pronounced as /d/ (11%) included irregular (e.g., *found*) and regular verb forms in which this suffix is pronounced as /ɪd/ after /t, d/ (e.g., *addicted*) or a modal verb (e.g., *could*). The wrong answers for <-ed> forms pronounced as /ɪd/ (11%) indicate that the informants only looked in their transcriptions for the /ɪd/ symbols without taking the morphological structure of the word into account. The erroneous examples included <-ed> forms articulated as /d/ (e.g., *cried*), a noun *side* and a contracted form *I'd*.
### 3.2.5 Responses for spelling-to-pronunciation patterns and other features

Letter-to-sound correspondences were less problematical for learners, with less than 7% of incorrect answers, as shown in Figure 5.

<caption>Figure 5</caption>

The Percentage of Four Types of Responses for Letter-to-Sound Correspondence

<figure><img src="image_74.png" /></figure>

Moreover, some spelling-to-pronunciation patterns displayed high levels of correctness, for example: the letter <i>as KIT (89%), <a> as TRAP (86%), the sequences <ea> or <ee> as FLEECE (86%), <ar> as START (57%). Others, although less frequently identified, were also mostly right, for instance <oo> as GOOSE (42%), <(w)or> as NURSE (28%), <or> as STRUT or NURSE (26%) and a suffix <-ous> as /-əs/ (13%). The suffix <-ate> as /-ət/ in nouns or adjectives or /-eit/ in verbs obtained the same number of right (7%) and wrong (7%) answers respectively.

The questionnaire also inquired about proper names, non-grammatical forms, non-standard pronunciation and accent-specific pronunciation features. Among 24% of reported proper names there were, for example: *California*, *Elton John*, *Kool-Aid*, *Rome*, *Peter Pan*, *Bon Jovi*, *Frankie*, and *Tommy*. The listing of non-grammatical forms and non-standard pronunciation was meant as a starting point for a discussion about dialects; however, the results revealed that the respondents did not understand what it involved. Apart from four good examples of non-grammatical forms (e.g., *He don't play for respect, I been, It don't matter, she live*), the respondents listed such features as: a) assimilated forms (e.g., *gonna*, *wanna*, *gotta*); b) the contraction *ain't*; c) interjections and exclamations (e.g., *ugh*, *oh*, *ooh*, *whoa*, *wooh*); d) the suffix <-ing> as <-in> (e.g., *keepin'*); and e) and short forms (e.g., *'cause*, *'bout*, *'til*, and *yes* as *ya*, *yeah*). Among the non-standard pronunciation variants, the following features of casual connected speech were provided: a) suffix <-ing> as <-in> -e.g., *knowin*, *gonna*); b) *t*-elision (e.g., *little bit*); c) a short form of *because* as *cos* and *about* as *'bout*; and d) *d*-elision (e.g., *ruled the*). When asked to list accent-specific pronunciation features, the informants limited their answers to either American or British English. The American features included: rhoticity
(e.g. *air*), LOT (e.g., *follow*), BATH (e.g., *can't*), *what's* as /wʌts/, the lack of a centring diphthong SQUARE in *compare* and NEAR in *year*, *worry* as /'wɜ:ri/ and *either* as /'i:ðər/. On the other hand, British features included: non-rhoticity (e.g., *California*), LOT (e.g., *everybody*), a diphthong NEAR (e.g., *dear*), BATH (e.g., *glances*), and a diphthong SQUARE (e.g., *nightmares*).

3.3 Attractiveness and usefulness of the task

After completing the task, 76 of 95 respondents evaluated its attractiveness and usefulness. As regards the first criterion the results on a 5-point scale are as follows: attractive (63%), neither attractive nor unattractive (28%), extremely attractive (8%), not very attractive (1%), not attractive at all (0%). The usefulness of the task scored higher marks: useful (68%), neither useful nor useless (21%), extremely useful (8%), not very useful (2%), not useful at all (0%).

The detailed responses on the usefulness of the task revealed a wide selection of reasons. Each student could provide more than a single reason. The respondents reported that they had learnt the pronunciation of words (n = 17), approached phonetics by doing something enjoyable (n = 17), and explored differences between BrE and AmE and singer-unique accents (n = 12). The exercise also helped them to notice the difference in an artist's pronunciation in songs and in speech (n = 5). They also worked on transcription (n = 9), became more aware of stress, rhythm, stressed and unstressed syllables, stress-shift (n = 7), weak forms (n = 7) and sounds (n = 4), including intrusive /r/ (n = 3) and linking /r/ (n = 3). They also felt that they had improved their own pronunciation skills (n = 4) and learnt that phonetics could be useful in life (n = 3). Other single comments remarked upon the opportunity to revise theory and terms, appreciate the complexity of English phonetics, encounter different types of morphemes, and focus on accent(s).

Nearly half of the respondents (49%) found the task more attractive than other phonetics activities, 9% expressed a contrary opinion and 39% were undecided. Seventy open-ended responses show that what they liked most about the activity was the fact that they could work with the text of their own choice (71%), which gave them more control over the assignment: “the free nature of the task, no obligation regarding the type and genre of the song” (S.51). Three respondents described it as an engaging and enjoyable task, which involved more creativity and imagination. Others appreciated the combination of work with pleasure (n = 5), found it a nice change from the usual exercises (n = 6) and a more approachable way to learn. They found it interesting to learn to transcribe in this way and to look at the lyrics more thoughtfully from a different perspective. They recounted that they liked the topic itself because of the variety of issues (e.g., linking /r/) (n = 3) and admitted that this activity broadened their knowledge. They had a chance to listen to different accents and to see how an accent may change (n = 3). They were also able to compare spoken English to singing. They reported that working on lyrics with music helped them to: focus and become involved in the task, pronounce lyrics/some words correctly, remember new material, understand phonetics issues in a better way and see how they could use phonetics skills in real life. They also stressed the social aspect of the activity, as they got to know the musical tastes of their groupmates, they themselves had an opportunity to express themselves with a song of their choice, and this even helped them bond with fellow students who shared their interests.

However, 24% reported disliking the fact that the activity was time-consuming, arduous, and repetitive, involving listening and analysing (n = 12). They also disliked its complexity because the scope of the analysis was too vast, with too many features to be examined (n = 7); the self-study nature of the task (n = 2); presenting this task in front of the class (n = 2). After university lessons shifted online during the pandemic, the respondents did not appreciate
presenting their work, because of the technical challenges involved in simultaneously playing
the audio and showing their transcription.

4 Discussion

This activity with a focus on form provided the students with opportunities to gain and revise
phonetic knowledge at their own pace. They exercised their analytical skills by working on the
lyrics of their own choice which they enjoyed.

The results show that the majority of the students were able to provide good examples of:
a) the vowels LOT, FLEECE, TRAP; b) the consonants /θ/, /ð/, /ŋ/ not followed by /g/; c) the
connected speech features such as weak forms, contracted forms, the article *the* pronounced as
/ðə/ before a consonant; d) the letter-to-sound correspondences <i> = KIT, <a> = TRAP, and
<ee> = FLEECE.

The respondents found this activity (extremely) attractive (71%), (extremely) useful (76%) and more attractive than other phonetic tasks (49%). Nevertheless, nearly one quarter of the cohort indicated the major drawback of this time-consuming task was that its scope was too large. What they appreciated most was the opportunity to exercise their phonetics expertise in a real-life activity, their autonomy in the selection of the material and the fact that they were able to learn phonetics by being actively involved in transcription and analysis, owing to their personal relationship with the song. According to the respondents, the major benefits of this task involved: learning the pronunciation of words, revising the theory of phonetics and focusing on accent(s) or the artist's non-standard pronunciation in a song. One respondent cited the example of Sting's northern rendition of BATH in British English in his 1993 song, The Shape of My Heart, where "he pronounces *the sacred geometry of chance* with *chance* as /tʃæns/ although he is British" (S.6).

There are limitations to the study and some shortcomings of the procedure. The results could
have been different if the ‘not provided’ category had been accounted for and if it had
constituted a separate category, not combined with ‘non- applicable’. The design could also be
improved to meet the students’ needs better, most notably by reducing the vast scope of the
task and the time needed for its completion. Thus, restricting the number of features would be
recommended.

5 Conclusion and implications

The foremost conclusion is that the following features present obvious challenges to Polish
university English first-year students: /ŋ/ followed by /g/, aspiration, <-.s> forms pronounced
as /ɪz/, KIT vowel, spread vowels, centring diphthongs, and schwa spelt <o>, <u>, <a>. These
features should therefore be practised several times during a course.

Through this activity, the respondents reviewed features of English phonetics and showed
whether they had learnt and understood the terms and concepts, which was the main aim. They
gained new skills such as typing transcription and phonetic symbols, making use of the online
transcription application Photransedit, and performing in front of the class or online, which
involved managing audio and transcription. They reflected on different pronunciation features
by paying attention to details when they compared canonical dictionary transcriptions with the
actual pronunciation of the artist.

The self-study character of the activity permitted them to revise the material at their own
pace. Using online tools for phonetic feature identification required autonomy and was meant
to raise students' awareness of phonetics. It is hoped that they became more confident in their
own phonetic expertise. In addition, they were encouraged to do some extra reading on an
artist's origin, place of residence and accent to verify whether their assumptions were right, which served as a prelude to a discussion on accent variation.

To conclude, this is a task which has its place in a phonetics course. Students appreciated being able to choose their own song to analyse, and the combination of words with music seemed to constitute a pleasant, entertaining element in their learning. As each student worked on their own text, the responsibility for providing good answers was shifted onto them. They could experience how phonetics works in the language and how they can apply it to examine speech and draw conclusions about an accent.

## References

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Good, A. J., Russo, F. A., & Sullivan, J. (2015). The efficacy of singing in foreign-language learning. *Psychology of Music*, 43(5), 627-640. https://doi.org/10.1177/0305735614528833

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## Appendix

### *A List of Most Frequently Chosen Songs*

Adele. (2008). Chasing Pavements [Song]. On 19. XL Recordings; London.

Adele. (2011). Set Fire to the Rain [Song]. On 21. XL Recordings; London.

Adele. (2011). Someone Like You [Song]. On 21. XL Recordings; London.

Adele. (2015). Hello [Song]. On 25. XL Recordings; London.

Adele. (2015). When We Were Young [Song]. On 25. XL Recordings; London.

Sheeran, E. (2013). I See Fire [Song]. On *Hobbit: The Desolation of Smaug*. Water Tower Music & Decca; Burbank & London.

Sheeran, E. (2014). Thinking out Loud [Song]. Asylum & Atlantic; New York.

Sheeran, E. (2017). Castle on the Hill [Song]. On Asylum & Atlantic; New York.

Sheeran, E. (2021). Bad Habits [Song]. Asylum & Atlantic; New York.

## About the author

**Marta Nowacka** teaches English phonetics and pronunciation at the University of Rzeszów in Poland. Her research concentrates on the Polish-accented speech of university students of English and the methodology of teaching phonetics to adults in an EFL context. She has co-authored two course-books on English pronunciation: *How much wood would a woodchuck chuck?: English pronunciation practice book* (Mańkowska et al., 2009) and *Sally meets Harry: Primer to English pronunciation and spelling* (Nowacka et al., 2011).

Email: mnowacka@ur.edu.pl
# A longitudinal study of individual difference in foreign language pronunciation development: The case of vowel production in Ecuadorian learners of English

Alejandra Pesantez
University of Zurich

Volker Dellwo
University of Zurich

This study investigated the development of the English vowel contrasts /i – ɪ/, /u – ʊ/, /ə – æ/, and /ɑ – ʌ/ produced by Ecuadorian Spanish speakers enrolled in the First and Second Language Teaching program at a State University in Ecuador. Recorded at six-month intervals at the end of the third, fourth, and fifth semesters, they produced 40 isolated English monosyllabic words. Each block of vowel contrasts had familiar words representing five words per vowel in a CVC and CVCC context. To examine pronunciation differences in the acoustic vowel space development over time, the Euclidean distance between the four groups of vowel contrasts was calculated based on F1 and F2 frequency values, which were Lobanov-normalised and analysed using a mixed-effects model. The results showed that Ecuadorian learners' pronunciation did not vary in the general learning trajectories over the three recordings, and did not match the native English criteria by the end of the study. However, Ecuadorian learners showed high variability *within* subjects and *between* subjects in the production of each vowel contrast over time. In summary, these results suggest that within-group, not all Ecuadorian learners followed the same developmental trajectories and varied depending on the vowel contrast.

**Keywords:** individual differences, L2 language, developing, vowel contrast

<figure><img src="image_75.png" /></figure>
# 1 Introduction

English is a widespread international language with many second language (L2) speakers from different countries who commonly differ on how accurately they produce English vowel sounds. This level of accuracy difference has also been reported in many studies pointing out that L2 English learners' difficulty in perceiving or producing specific sounds (consonants or vowels) can vary depending on the first learners' language (Flege et al., 1997; McAllister et al., 2002). Furthermore, several studies on group observation have reported that L2 learners' pronunciation improvement may occur at the beginning of the learning process in a naturalistic environment (Flege et al., 1997; Thomson & Derwing, 2015). Although research on group data is highly informative, we believe there is much to be gained by examining individual differences in L2 pronunciation in a non-naturalistic setting. To identify specific predictors of phonological acquisition, studies on L2 learners' variability have focused on learner-internal factors such as the role of awareness in pronunciation, motivation, language use, anxiety, aptitude, among other factors (Baran-Lucarz, 2014; Elliott, 1995; Saito et al., 2019). However, the results of these studies reflect neither learner-external factors such as the quantity and quality of input and opportunity of language use (e.g., Flege & Wayland, 2019; Moyer, 1999; Muñoz, 2011), nor their impact on learner's developmental trajectories (Pesantez & Dellwo, 2022). Moreover, literature on L2 individual variation is limited and focused more on L2 perception (Kim et al., 2018; Kogan & Mora, 2022; Mayr & Escudero, 2010; Morrison, 2009; Munro et al., 2015) than L2 production (Lima, 2019; Munro et al., 2015). For example, Morrison (2009) found that L1 Spanish L2 English listeners who had exposure to the same Canadian English dialect followed an indirect developmental path in perception patterns. Furthermore, Munro et al. (2015) found high variability in consonant clusters for both onset and coda developmental performance within Mandarin and Slavic learners of English.

The current study extends previous longitudinal research on the development of English vowels produced by Brazilian learners (Lima, 2019). Both Portuguese and Spanish learners of English have problems producing vowel contrasts (Rallo Fabra & Romero, 2012). Data from several studies suggest that L1 Spanish speakers rely on vowel duration as acoustic cues to make difference between vowels, even though contrast on durational cues does not exist in the Spanish language (e.g., Escudero & Boersma, 2004). We will explore longitudinal learner performance on the English vowel contrasts /i - ɪ/, /u -ʊ/, /e - æ/, and /ɑ - ʌ/ produced by a group of L1 Ecuadorian (EC) Spanish speakers. They share the same number of years of exposure in a classroom setting and have the same exposure to Spanish-accented English teachers. The choice of these vowel contrasts is intentional as they are crucial for L2 English intelligibility (e.g., Levis, 2020; Munro & Derwing, 2008; Rallo-Fabra & Romero, 2012). The study also explores individual learner development, i.e., it assesses L2 learners on their own individual pronunciation problems instead of focusing on group errors.

# 2 Methodology

## 2.1 Research questions

The study aims to answer the following research questions:

**RQ1:** Do Ecuadorian learners vary in their general learning trajectories (i.e., pronunciation changes over time) as they advance in their studies?

**RQ2:** To what extent do Ecuadorian learners vary in their developmental trajectories across the four groups of vowel contrasts?
## 2.2 Participants

Two groups of speakers were analysed, an L1 English group and an L2 English group. The first group was made up of six L1 American English (NE) speakers who were studying Spanish as a second language in Ecuador, including one who was an English teacher. The three females and three males had a mean age of 23.6 years (range: 19–43). The L2 group consisted of twenty-four undergraduate students enrolled in the First and Second Language Teaching program at a State University in Ecuador. They were L1 Ecuadorian (EC) Spanish monolinguals who had never been in an English-speaking country. In this paper, we present only the data for the female participants (n = 16), because vowel formant ranges vary with gender. Their ages ranged from 18 to 28 years old with a mean of 21.13. Fourteen participants reported having not studied any foreign language other than English, but two participants had studied Portuguese and Quechua. They also had experience with English learning, having had four hours per week at secondary school in Ecuador with non-native English input most of the time. All participants gave their written informed consent at each recording session and were paid for their participation.

## 2.3 Description of the learning context

The participants were recorded at six-month intervals at the end of the third semester (recording 1), fourth semester (recording 2), and fifth semester (recording 3). Each semester had sixteen weeks of classes (excluding weeks of mid-term exams and final exams). They studied Research Methodology, TEFL, Communicative Grammar, Reading, and Writing – among other courses – from the first to the fifth semesters, as well as Phonetics in the fourth semester and Phonology in the fifth semester. In Phonetics, the participants learnt about the sounds of the English language without a context; they focused on using phonetic symbols (vowels and consonants) to understand English pronunciation. In Phonology, they focused on the organisation of English sound patterns and those present in L2 English speech. Over the period of the three recordings, the participants had only the L2 input from L1 Spanish teachers of English in their classes. At the end of each testing session, they completed a general language background questionnaire to measure their exposure to the English language in the classroom (number of English courses taken), input (native or non-native), and the frequency of their use of English.

## 2.4 Production task

The experiment was conducted at the radio station of the State University in Ecuador. The participants produced 40 isolated English monosyllabic words with no carrier phrases. The instructions were written in English and appeared on the first slide. Each vowel pair tested 10 familiar words: five words per vowel in a CVC and CVCC context, and 5 disyllabic words added as distractors. The test words included:

* /i - ɪ/: sheep-ship, cheap-chip, keys-kiss, feet-fit, seat-sit;

* /u -ʊ/: boot-book, fool-full, pool-pull, food-foot, suit-soot;

* /ɛ - æ/: bet-bat, pen-pan, men-man, send-sand, beg-bag;

* /ɑ - ʌ/: dog-dug, hot-hut, caught-cut, boss-bus, lock-luck.

A Zoom H2n Handy recorder at 44.1 kHz sampling, 16-bit quantisation was used for the three recordings. Before starting the recordings, participants were familiarised with the task in a preliminary trial, where the presented words were unrelated to the words of the study. No recording was made during the familiarisation phase. Using a computer screen, the pictures
were presented in a random order (mostly tense vs. lax vowel) with their Spanish translation
next to them, to avoid the effects of orthography in the production of the English segment
phonemes (Bassetti, 2017; Flege et al., 1997). In cases where the participants did not say the
expected word, the researcher said “No” and the participant could self-correct by saying the
correct word. All the participants repeated each word two times to counter frequent hesitations
during the initial production. They were asked to say the words in a natural way and at a normal
volume. NE speakers followed the same procedure.

**2.5 Acoustic measurements**

Our approach to evaluating L2 speakers' performance relies on Euclidean distance (ED) values of the vowel contrasts. We compared the ED values with values from English native speakers which served as baseline. However, the native speakers' data was not used in the statistical models. The NE values were only used for comparison, as the aim of the study was not to test participants' native-likeness.

To compare the individual changes in the spectral characteristics over time, the onset and
offset of each vowel segment were manually annotated by following standard phonetic criteria,
using waveform, spectrogram, and auditory discrimination cues in Praat (Boersma & Weenink,
2022). The F1, F2, and F3 formants means were extracted using an automatic formant
measurement method in Praat. The same parameters used by Kartushina and Martin (2019)
were also applied in this study. The F1 and F2 mean values obtained by each vowel setting
were examined to see if their values were within the speaker's vowel formant gender ranges.
F1 and F2 formants were Lobanov-normalised by semester and grouped by speakers using the
visible vowel website (Heeringa & Van de Velde, 2018). The ED between the four groups of
vowel contrasts was calculated based on F1 and F2 frequency values. Then the ED values for
each vowel contrast were plotted to trace individual vowel development over time (see Figure
1). The distance values were fitted into a linear-mixed effects model to the data.
<caption>Figure 1</caption>

*Mixed-Effects Model of the Sum of ED of the Four Pairs of English Vowels by Recording for Each Speaker*

<figure><img src="image_76.png" /></figure>

three dots = ED blue line = within-subject red line = mixed model

black dashed line = group dotted line = native speaker

*Notes.* Time is ordered, from left to right. The speaker code-letter is in the strip above each panel.

3 Results

R core Team (2022) was used for statistical analysis and figure plotting. For the purposes of RQ1 (i.e., to look at the variability in the general vowel development of the group), the sum of the ED of the four target pairs of vowels was used to fit a linear mixed-effects model (LMM) to the data. The fixed effect was coded as Recordings, and a random effect for subject and a random effect for the slope with respect to recordings (see Bates, 2010, for a review). In Figure 1, the three black dots are the sums of ED for each recording, and the blue line (which is the tendency line for each speaker from a simple within-subject linear model) indicates little evidence of vowel development. The general tendency of the group (the black dashed line) which is repeated in every individual plot, does not reflect an increasing slope over the three recordings. The red line is the result of the LMM fitted for each speaker's data. It indicates no subject-to-subject variation over the three recordings. Finally, the dotted line, with no slope and repeated in all individual plots at 3.96, marks the sum of the ED of the six NE speakers showing how each participant differs from NE data. The results of the LMM showed no significant individual difference in the general learning trajectories over the three recordings *F* (1, 28.79) = 0.0041; *p* > 1.

We can also observe different learning trajectories within-subject (the blue line). For example, of the sixteen EC participants, six started with ED values comparable to the mean of the group and four of them showed no improvement in recording 2 and 3 (B, D, G, O). Interestingly, only three participants improved the ED values over the three recordings (I, J, P). Nonetheless, there were some participants who decreased their ED values (E, F, K, L, M) over the three recordings. Furthermore, four participants got ED values above or below the
population (A, C, H, N). In general, these data suggest that there are all types of L2
developmental routes within subjects, even though in the group no general different change
rate was found.

For the purposes of RQ2, i.e., to explore how EC learners vary in their developmental trajectories across the four groups of vowel contrasts, we used an LMM per each vowel contrast. Figure 2 shows changes in the ED values (three black dots) per each participant, and with the tendency to increase (blue line) for some participants (simple within subject linear model) for the contrast /i - ɪ/ and /ɑ - ʌ/, a clear trend of decreasing for the /æ - ε/ and a steady trend for /u - ʊ/. What stands out in these figures is the difference in the subject-to-subject variation over the three recordings (red line, representing the result of the LMM fitted for each speaker's data) and the variability within-subject with respect to the general tendency of the group. Finally, the dotted line with no slope for the /i - ɪ/, /u - ʊ/ (left panels), for /ɑ - ʌ/ and /æ - ε/ (right panels) represent the NE's ED at 1.66, 1.22, 0.66, and 0.38, respectively. The results of the LMM were significant for the /i - ɪ/ F (1, 38.015) = 10.381; p < .01, and for the /æ - ε/ F (1, 31) = 11.239; p < .01, but not for the /u - ʊ/ F (1, 44.372) = 1.271; p > 1, and /ɑ - ʌ/ F (1, 42.651) = 0.0637; p > 1.
<caption>Figure 2</caption>

*Mixed-Effects Model of ED for the /i/-/ɪ/, /æ/-/ɛ/, /u/-/ʊ/ and /ɑ/-/ʌ/ of the English Vowels by Recording for Each Speaker*

<figure><img src="image_77.png" /></figure>

*Note.* Time is ordered, from left to right. The speaker code-letter is in the strip above each panel.
Of the four groups of vowel contrasts, the /i - ɪ/, /æ - ε/ and /ɑ - Λ/ showed higher within-subject variability in their L2 production. As observed in Figure 2, for the /i - ɪ/ contrast, six participants (B, D, G, K, N, O) started with ED corresponding to the mean of the group and only four participants followed the developmental performance of the group. Interestingly, only one participant (I) reached NE standards in recording 3. For the contrasts /æ - ε/, three participants (B, F, I) started with ED comparable to the mean group and three followed the developmental performance of the group. For the /ɑ - Λ/ pair, there were three participants (C, E, I) who started with ED similar to the group mean, but only one (I) followed the development of the group. For the /u - ʊ/ pair, most of the participants started and kept the development of the group, but only four participants slightly increased the ED over the three recordings (A, I, L, N). These findings mean that these participants show higher variability in L2 speech development in front vowel contrasts than the back vowels over the three recordings.

## 4 Discussion

Our study provides evidence of EC learner's pronunciation not changing over time. This finding is consistent with Herrero and Delicado (2022) whose L2 learners also showed minimal change in the quality of the vowels. The relatively stable L2 productions in the sum of the ED found here may reflect that EC learners need more years of English instruction to improve their L2 production of the vowel contrast (Muñoz, 2011). However, it might be also related to a degree of early fossilisation that occurred during the first or second semester, as has been reported in other studies (Derwing & Munro, 2014; Munro & Derwing, 2008). To determine which vowel contrasts show high variability in the developmental performance between EC learners, we analysed each vowel contrast separately. The results for the front vowel contrasts showed statistically significant differences in learners' developmental path, but not for the back vowel contrasts. For the contrasts /æ - ε/, we expected that EC learners would start with larger spectral difference than NE speakers due to the influence of Spanish /e/ in the English /ɛ/ production. This finding is consistent with that of Flege et al. (1997), where Spanish participants produced larger spectral differences than did the group of native speakers. The results in our study differ from Lima (2019) who found that most participants created separate categories for the vowel pairs over the four recordings and maintained them in most cases. In our study, participants did not produce vowel contrasts with similar ED values to NE speakers over the three recordings, and those who could increase the ED values lost them in the second or third recording. Moreover, in Lima's (2019) study, many participants started in the first recording by producing a vowel contrast for the /i - ɪ/, whereas none of our participants started with higher ED values in recording 1 for this vowel pair. However, both studies show the high degree of variability in L2 acquisition and in the type of developmental patterns L2 learners follow. We did not analyse intra-subject variability statistically per each vowel, but as Figure 2 shows, most EC learners vary in their own developmental trajectory (blue line) for the vowel pair /ɑ - Λ/. For example, speaker P started with an ED (three dots) comparable to NE speakers in recording 1, changed the order in recording 2, and surpassed NE in recording 3.

In general, this study showed that over the three recordings fewer than four participants followed the mean trend of the group for each vowel pair. Given this finding, a more individualised approach would be a more effective pedagogical strategy for these speakers, especially for /i - ɪ/ and /æ - ε/. In the context of foreign language acquisition with major L2 foreign-accented input, it would be better to start with phonetics and phonology classes from the beginning of instruction to avoid fossilised errors in pronunciation. In addition, integrating applications of automatic speech recognition into pronunciation instruction can help teachers
to pinpoint problems in L2 sounds, and at the same time make L2 learners aware of their own pronunciation challenges.

## 5 Conclusion

This study explored EC speakers' variability in the development of eight English vowels in an instructed foreign language context, with most input supplied by non-native English teachers over five semesters. In most such classrooms, L2 learners have major exposure to foreign-accented input. The nature of that L2 input is crucial in the process of speech learning, with adults' performance depending on the quantity and quality of input (Flege & Bohn, 2021). Studies tracking L2 learners and their non-native English teachers might give a better understanding of how foreign-accented input activates L2 phonetic acquisition.

Valuable insight is gained about learners of English in the instructed foreign language context of Ecuador, a country underrepresented in the field of L2 English. However, the small sample size means that conclusions cannot be generalised to all L1 Spanish learners. Nonetheless, this paper contributes to an important line of L2 pronunciation research related to individual differences and their changes over time, showing that not all vowels are acquired at the same rate and that very few learners follow the mean of the group. More research with larger sample sizes and more words representing each vowel needs to be conducted in both perception and production, to further examine long-term L2 development.

## Acknowledgements

This project was funded partially by the University of Zurich-GRC (Graduate Campus) Travel Grants, 2019-2020.

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**About the authors**

**Alexandra Pesantez** is a PhD student at the Computational Linguistics Lab-University of Zurich. Under the supervision of Professor Dr. Volker Dellwo, she is carrying out a longitudinal study to explore Ecuadorians' L2 language speech development. Through acoustic analysis, she has examined which English vowels are easily acquired and which ones are acquired at a later stage. These data are part of an extensive project on foreign language acquisition.

Email: alejandra.pesantzpesantez@uzh.ch

**Volker Dellwo** is chair of Phonetics at the University of Zurich. He is interested in a wide variety of phenomena related to human and machine communication with speech. Most of his research is about speaker specific (indexical) information in the speech signal and how humans and machines can use such information to recognise individuals and/or process speech.

Email: volker.dellwo@uzh.ch
Rupp, L., Das, A., Kamps, A. & Acosta, E. (2023). English Pronunciation in a Global World: A MOOC course for pronunciation teaching. In A. Henderson & A. Kirkova-Naskova (Eds.), *Proceedings of the 7th International Conference on English Pronunciation: Issues and Practices* (pp. 225–235). Université Grenoble-Alpes. https://doi.org/10.5281/zenodo.8225221

# English Pronunciation in a Global World: A MOOC course for pronunciation teaching

Laura Rupp

Vrije Universiteit Amsterdam

Amrita Das

Vrije Universiteit Amsterdam

Alisah Kamps

Vrije Universiteit Amsterdam

Ericka Acosta

Vrije Universiteit Amsterdam

The paper presents the Massive Open and Online Course (MOOC) English Pronunciation in a Global World (Rupp, 2019). We outline the context in which the MOOC was created, one in which English is a global language yet the majority of the world's population do not speak English (Crystal, 2017), and one in which linguistic misunderstandings occur and accent discrimination persists (Lev-Ari, 2021). We outline the aims of the MOOC and describe how we sought to achieve these aims using linguistic and psychological theories (Derwing & Munro, 2015; Ryan & Deci, 2000), as well as methods from the Mixed Classroom Model in Practice (Ramdas et al., 2019). We report on the findings from running the MOOC thus far, some of which we anticipated (e.g., the new type of pronunciation data it generates) and some of which were not immediately foreseen (e.g., participants spontaneously mentoring other participants). In the discussion we reflect on the advantages and challenges associated with using a MOOC for pronunciation teaching, and address the opportunities that it provides.

**Keywords:** Massive Open and Online Course (MOOC), English pronunciation, inclusive English education, English accents, English pronunciation research

<figure><img src="image_78.png" /></figure>

This chapter is based on the oral presentation given by the authors at the 7th International Conference on English Pronunciation: Issues and Practices (EPIP 7) held May 18–20, 2022 at Université Grenoble-Alpes, France. It is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of the license, please go to: http://creativecommons.org/licenses/by/4.0/.
# 1 Introduction

English is the most widely spoken language in the world and using English as a Lingua Franca has provided numerous opportunities for people to connect to each other. However, many ‘disconnections’ have arisen at the same time.<fnref n="1" /> First, estimates of the number of English speakers in the world (Crystal, 2017) suggest that the majority of the world’s population cannot speak (much) English because they have not had the opportunity to learn it. Second, speakers of English speak with various accents, from which linguistic misunderstandings may arise. One specific example that led to a near-conflict happened in 2008, when Bernard Kouchner, the then French minister of Foreign Affairs, warned that one country would ‘eat’ another country. This sounded very aggressive and caused a great upset. Kouchner had wanted to say ‘hit’ but he had dropped ‘h’ and pronounced a different vowel, as many French speakers would do. He felt compelled to apologise for the phonetic confusion that he had caused.<fnref n="2" /> Third, accents are associated with particular personal characteristics and this may lead to discrimination based on accent (Lev-Ari, 2021). For example, testimonies of witnesses have been discredited because of the way they speak English.<fnref n="3" />

# 2 The MOOC and its aims

In this context, we created a Massive Open and Online Course (MOOC) called English Pronunciation in a Global World to see whether it could be an instrument in addressing these three matters related to English pronunciation. The MOOC therefore has three aims, both linguistic and social:

1. Aim 1 – To provide an academic, research-informed course on English pronunciation that is freely accessible and can be attended by anyone in the world on any device.

2. Aim 2 – To enhance linguistic understanding of both learners’ own accents and variation in English accents to help prevent misunderstandings. Here we adhere to the Intelligibility Principle: “The notion that the goal of pronunciation instruction should be to help learners to become more understandable by focusing on those aspects of an accent that interfere with listener comprehension.” (Derwing & Munro, 2015, p. 178)<fnref n="4" />

3. Aim 3 – To enhance sociolinguistic appreciation of variation in English accents to help combat social issues associated with English pronunciation e.g., accent discrimination. To quote one MOOC participant<fnref n="5" />: “The most remarkable thing that I learnt ... is the fact that it's ok for me to have my own identical accent ...I'm glad that maybe I won't feel so embarrassed now every time I speak aloud.”

At a more general level, our aims relate to three larger goals:

1. The United Nations Sustainable Development Goals (UN SDG) #4 Quality Education (Ensure inclusive and equitable quality education and promote lifelong learning

<footnote n="1">¹ In this paper, we will not venture to address the reasons for this, one of which has to do with the colonial roots of English. See Philipson (1992) for discussion and the notion of linguistic imperialism.</footnote>

<footnote n="2">² http://news.bbc.co.uk/1/hi/world/middle_east/7658728.stm</footnote>

<footnote n="3">³ https://www.linguisticsociety.org/sites/default/files/Rickford_92_4.pdf</footnote>

<footnote n="4">⁴ Levis (2005) was the first to introduce the notion of Intelligibility Principle.</footnote>

<footnote n="5">⁵ All examples from MOOC participants are quoted in their original version. Grammatical errors and non-idiosyncratic word choices have not been corrected in the text.</footnote>
opportunities for all) and #10 Reduced Inequalities (Reduce inequality within and among countries).<fnref n="6" />

2. Sociolinguistic principles like Labov's (1982) Principle of Debt Incurred, that "Linguists have an obligation to expose misunderstandings and misinterpretations about language to the attention of the widest possible audience" (p. 173).

3. Community engagement seeking to "generate activities that build capacity and affirm human dignity through sustainable and reciprocal [emphasis added] collaborations with communities who experience disadvantage or marginalisation."<fnref n="7" />

## 3 Previous research on the topic

One important tenet of the MOOC is that it should be informed by academic research. Therefore, to achieve our aims in the creation of content for the MOOC, we took account of linguistic research on the intelligibility of, and attitudes towards (Beinhoff, 2013), pronunciation features in speakers of English. Regarding intelligibility research, we were aware of the difficulty of establishing the intelligibility of pronunciation features in the tradition of Jenkins's (2000) Lingua Franca Core (see Henderson, 2008, p. 6); nonetheless we felt the best approach was to take into account the evidence that has been reported in the literature, expressing appropriate caveats in the MOOC. We adhere to the distinction proposed by Derwing and Munro (2009) whereby "accent is about difference, comprehensibility is about the listener's effort, and intelligibility is the end result: how much the listener actually understands" (p. 480).

Furthermore, to ensure successful learning, we considered psychological and educational approaches to learning. First, to foster motivation and development in the learners, in the absence of a physically present teacher, we deployed Ryan and Deci's (2000) Self-Determination Theory (SDT). This psychological theory suggests that people have three needs, i.e., a sense of competence, relatedness, and autonomy; when these three needs are fulfilled, they can make choices and stay motivated. Second, to ensure that the diverse group of learners in a MOOC would form a community, engaging with one another and developing together, we deployed the VU Mixed Classroom Educational Model (MCEM). This educational approach "builds upon differences to enrich the learning experience for all students present" (Ramdas et al., 2019, p. 5). It works in a three-phase process (Ramdas et al., 2019, p. 9):

1. *Sensitising* – students need to be sensitised to their own frame of reference and the existing diversity in the classroom, and a safe learning environment to do so should be created;

2. *Engaging* – students should interact constructively with different perspectives present in the classroom; and

3. *Optimising* – every student's learning process should be optimised by having them capitalise on different perspectives and approaches.

Thus, the MCEM seeks to “to open up to differences, to co-create an inclusive environment, and capitalise on different perspectives in order to create value” (Ramdas et al., 2019, p. 5).

<footnote n="6">⁶ https://sdgs.un.org/goals</footnote>

<footnote n="7">⁷ https://www.acu.edu.au/about-acu/community-engagement</footnote>
## 4 Research methodology

To achieve the aim of providing inclusive English education, we ran the MOOC on the platform FutureLearn.<fnref n="8" /> In FutureLearn, learners have free access for the duration of the course (four weeks) and the following six weeks.<fnref n="9" /> More importantly, FutureLearn offers technical support, ensuring that the course can be attended on any device anywhere in the world. Adhering to the MCEM's sensitising phase and the SDT component need of relatedness, we created an inclusive learning environment through introductory activities in the MOOC in which the learners get to know one another. For example, learners share their English accents by posting audio clips on a virtual Padlet wall (see Figure 1).

<caption>Figure 1</caption>

English Accents Wall in the MOOC: Learners Upload Recordings to Introduce Themselves

<figure><img src="image_79.png" /></figure>

On another Padlet wall, learners can voice prior experiences speaking English. They find commonalities with other participants such as employment prospects, educational ambitions, or a lack of confidence in speaking English, as in the examples (1a) and (1b) from two MOOC participants:

(1a) Hi, I'm from Malaysia. ... I'm from the city that mainly speak Chinese thus my pronunciation is not that proper and afraid to talk in English.

<footnote n="8">⁸ https://www.futurelearn.com/. One advantage of the FutureLearn platform is that one can make use of their experience regarding online learning, expressed as four pillars of social learning: 1) telling stories; 2) designing for conversation; 3) developing skills; and 4) celebrating progress.</footnote>

<footnote n="9">⁹ For longer access and other advantages, learners need to purchase an upgrade. However, we seek as much as possible to distribute upgrades to disadvantaged learners (amongst others, Sakhi for Girls Education; https://sakhiforgirlseducation.org), as well as via our mentor programme (see §7). Note further that material in the MOOC that we have created ourselves is Creative Commons (BY-NC-CA). This material can be freely downloaded and adapted, as long as the source is acknowledged. We hope that English teachers find the materials useful for their own English classes.</footnote>
(1b) Greetings! I'm from India and as you all know India had been a British colony and their influence is clearly seen in India, even after 75 years of independence. At present it has become kind of necessary to learn English here in India otherwise you end up being the inferior one.

Learners can join our social media channels and thus remain part of the MOOC's community of learners also after completing the course.<fnref n="10" />

The introductory activities are addressed in week 1 and are part of the first main topic, i.e., what is important in English pronunciation. The other three topics in weeks 2, 3 and 4 are: English vowels, English consonants, and suprasegmental features. Each week contains a number of steps. Each step contains a type of activity that helps achieve the MOOC's aims of enhancing participants' understanding of English pronunciation features and their appreciation of different English accents. At every step, learners can meet and engage with other learners, share their ideas, join in by leaving comments, adding their perspectives. For instance, in the activity 'Discussions', learners learn about linguistic and social aspects of English pronunciation, in particular the notions of intelligibility, credibility, and identity in English. There is also a range of practice activities in the MOOC that contain, amongst other tools: explanatory videos (which explain the IPA and why English sounds different from the way it is written by outlining phonological changes that have occurred in the history of English), listen-and-repeat exercises, analytical assignments of real or near-real life data, quizzes, etc. By introducing learners to a variety of practice tools, we hope to boost their autonomy (in line with Ryan and Deci's (2000) Self-Determination Theory - SDT) and to empower them by showing where to look for such tools themselves, so they can continue to develop their pronunciation after the course.<fnref n="11" /> Also, there is a peer-review exercise in which learners: 1) set pronunciation goals for themselves according to the kind of accent they wish to develop; 2) make a recording of a word list and a reading passage at the beginning of the course; 3) make a second recording at the end of the course, focusing on their goals and attempting to apply what they have learnt; and 4) peer-review one another's recording. In this way we aim to enhance learners' feelings of competence (consistent with SDT) and to optimise their learning from one another (consistent with MCEM) so that they: 1) they take agency over their own accents; 2) find out on their own about differences between their accents and others'; 3) help others by giving feedback regarding what is difficult to understand and what they like about the other person's accent; and 4) support and encourage each other. Examples (2a) and (2b) illustrate these endeavours:

(2a) I noticed you didn't pronounce the final -es in words like 'increases' and 'focuses'.

(2b) Once again, congrats!

Finally, the MOOC team act as mentors, offering support to learners by responding to a selection of posted comments. In two runs every year, we facilitate the course, which means that we offer participants the opportunity to directly communicate with us, thus providing a bridge between online learning and the classroom. For example, we hold an online live session in which learners can join us and we answer questions, reflect, and award the winner of the most insightful comment.

<footnote n="10">¹⁰ www.facebook.com/globalenglishlearn; www.instagram.com/moocenglishpro.</footnote>

<footnote n="11">¹¹ The MOOC draws as much as possible from available material from the Internet that is of good quality, and has complemented this material with self-created material.</footnote>
**5 Results and discussion**

In three and a half years, the MOOC has reached over 117,000 learners from over 191 countries (see UN SDGs #4 and #10). We have seen a great deal of interaction amongst the learners, with some of the comments liked around 200 times and giving rise to threads of nearly 60 comments. Interestingly, learners are not only looking to us for advice or help, but also acting as a community and giving each other advice and helping each other out, an unexpected level of collegiality. For example, in (3a), one MOOC participant explains the pronunciation feature of voicing to another learner, and in (3b), one participant explains how to upload their recording.

(3a) A: I got confused between /t/ and /d/ sound and also /b/ and /p/.
B: If you put your hand on your throat and say first /p/ then /b/ you'll feel the throat vibrate on /b/... the same with /t/ and /d/, the first is voiceless and the second voiced.

(3b) A: How can I share my audio. I recorded through speak pipe. But I don't know how to send.
B: ... did you ... If so, you can share that link here in the comments.

Regarding our Aim 2, we found that the MOOC participants developed insights about intelligibility, and optimised their understanding of pronunciation features by capitalising on differences between their accents and those of fellow learners (see the MCEM's optimising phase). The examples (4a) and (4b) illustrate this point:

(4a) The course brought me a greater awareness of different accents. The accent itself is not a mistake, as long as it meets certain pronunciation features necessary for good communication. It made me lose some fears, and risk more to speak without fear. I believe that this can lead me to improve the pronunciation of the English language.

(4b) I have realised that the position of stress is different from mine.

Regarding our Aim 3, we found that the MOOC participants constructively interacted with each other to derive a nuanced understanding of the notion of credibility and the prejudice that goes with it (see the MCEM's engaging phase). The following examples illustrate and exchange of ideas beginning with an insight into this topic (5) and then a discussion of it between different learners in a thread (6a, 6b, 6c, 6d):

(5) I often have the feeling that people question my knowledge comparing to people with better pronunciation and fluency. Until now I never read there was actually some research on credibility and accent relation. At least I understand it better and I feel my perception of those situation was correct.

(6a) Yeah, I was also impressed about accent affecting credibility. When I watched the video, I considered what happens in my country and I agree about accent causing discrimination. ... Actually, these pronunciation features are wrongly related to ... laziness, illiteracy ...

(6b) I guess it is something seen and experienced in many places (if not all). Where people are judged (criticised) on the basis of their accents.
(6c) I never thought about it ... I did not want to change my accent because I am Spanish, what can I do? I have a Spanish accent ... However, recently I am finding it challenging to progress in my career, and I think language might be something that could help me. Everyone says “what a lovely accent you have” but I am not sure is in a patronising or genuinely way.

(6d) Around the world happens the same discrimination about the bad accent ... we could be better people because, in this way we study to understand the need[s] of other people from different places and help them[,] like in this group. More educated day after day.

To summarise, we have created an academic, research-informed MOOC that is freely available (Aim 1). We examined if a MOOC could help make English education accessible and enhance understanding of English pronunciation features and appreciation of different English accents. We believe the results discussed in this section support these ideas. In this context, the three key findings are:

1. The MOOC reaches out to tens of thousands of learners around the world (contributing to the UN SDGs #4 and #10), who interact with the material in the MOOC and with one another (Principle of Community Engagement; see also §6);

2. Learners express understanding of the notion of intelligibility, and of the pronunciation features that affect the intelligibility of their own English accents and that of other speakers of English (see Aim 2);

3. Learners express understanding of the notion of credibility, and an appreciation of diversity in other English accents (see Aim 3).

In this way, a MOOC can play an important role in Labov's (1982) Principle of Debt Incurred.

# 6 Conclusion and implications

Having concluded that a MOOC is a suitable tool for making English education accessible, and for enhancing understanding of English pronunciation features and the appreciation of different English accents, we can now apply the MOOC in various ways, both in pronunciation research and in pronunciation teaching.

In research, the MOOC provides a new type of data. Comments that participants make in the MOOC are self-reported and not affected by the presence of a teacher or a native speaker. Therefore, they provide a window on various aspects of pronunciation features directly from the perspective of the learner, for example revealing the salience of pronunciation features. A broad definition of salience is that it concerns linguistic features that are ‘noticeable’ or ‘stand out’. Taking into account work by Trudgill (1986), Auer et al., (1998, p. 167) distinguished between two types of salience: objective and subjective. Objective salience involves language-internal properties, such as the phonetic distance or phonological contrast between two pronunciation features. Subjective salience involves extra-linguistic properties, such as a feature being a high-prestige variant or an overtly stigmatised or stereotyped one. We conducted a pilot study amongst Spanish speakers of English, examining their responses to: 1) a step in the MOOC in which they set pronunciation goals, and 2) one of the questions in a questionnaire concluding the course (What feature(s) of your English accent would you still like to improve? Why?). In 235 responses from Spanish speakers of English, we found that 25% put rhythm and stress first (Kamps, 2021) — suprasegmental features which apparently stood out for the learners but may not fit either category of salience straightforwardly.
Therefore, we could explore MOOC data to gain more insight into the nature of a notion such as salience. Additionally, it has been argued that salience plays a role in a number of linguistic processes, such as language change, dialect accommodation and second language learning (see the overview in Jansen, 2014). Accordingly, we could take account of features that learners conceive of as salient themselves when we prioritise the pronunciation features we teach. Furthermore, we may use the MOOC data to probe other pronunciation issues, such as the perceived intelligibility of particular pronunciation features or the comprehensibility of English accents. For instance, in one step in the MOOC, learners learn about rhoticity and comment whether they consider rhotic or non-rhotic English accents more comprehensible, as shown in examples (7a), (7b), and (7c):

(7a) Rhotic speakers are easier to understand (MOOC participant from the Philippines)

(7b) The non-rhotic is very British. I like it! But it's a little bit difficult to understand. (MOOC participant from Spain)

(7c) I lived in New Zealand for 4 years, and they have a non-rhotic accent. It was difficult to understand ... (MOOC participant from Chile)

Another application of the MOOC in pronunciation research is how we can learn about the pronunciation of English by speakers of languages that are less familiar. For example, one of the MOOC participants commented that "My language Ibibio, spoken across the Niger Delta region of south Nigeria does not have the /3:/ sound". Such a comment illustrates the way in which the MOOC is meant to constitute a true form of Community Engagement; we share the pronunciation material that we have with learners, and conversely, we learn from our learners and can improve the content of the MOOC through the input they provide.

Third, research into e-learning could be conducted, by setting up studies in which the MOOC is used in pronunciation teaching in an experimental group and a comparison group receives in-class pronunciation teaching. Some steps in the MOOC are suitable for evaluating learners' progress in the learning of pronunciation features and the understanding of other speakers' accents. Examples of this are steps in which learners make a recording of their English pronunciation and peer-review the recording of another learner, respectively, and steps where they make a second recording and conduct a second peer-review, applying what they have learnt in the course.

In teaching, we have capitalised on the finding that MOOC participants provide feedback to other participants of their own accord. Namely, we have established a network of external mentors, contacting MOOC alumni in the first instance, and now drawing from a pool of MOOC alumni who volunteer as external mentors in a Google form in the MOOC. We aim to have external mentors speaking a range of different native languages because they will be able to provide tailor-made feedback to learners who share the same native language (as well as providing expert input to the MOOC that we do not have ourselves). External mentors give feedback in the MOOC for four weeks and write a reflection of their experiences as a MOOC mentor; they are then awarded an upgrade to the MOOC and a certificate. More recently, we have engaged current university students in mentoring, too, offering them a teaching opportunity and hoping for them to gain valuable experiences. According to the 'Lernen durch Lehren' principle (Martin & Oebel, 2007), students may learn about pronunciation differences by trying to explain these to others. Furthermore, students may struggle to make their theoretical knowledge feel tangible and may not feel educated enough on a topic to legitimately join the conversation. Mentoring in the MOOC may help to bridge that gap for them, because
they get first-hand experience of pronunciation research pursuits and the effects of this research on both a real-life curriculum and, importantly, in real people's lives.

The importance of native speakers also participating in the MOOC should not be forgotten, as one observed: “Knowing what I know now from this course I am so grateful to be a native English speaker. I had no idea of the intricacies of pronunciation and the challenges posed to learners of English in this regard.” Real-world communication involves speakers from diverse backgrounds and proficiency levels, so encompassing such diversity with the MOOC is key to truly achieving inclusion and promoting understanding.

## Acknowledgments

We would like to thank the students from Concordia University, Canada and Université-Grenoble Alpes, France, who have been working as external MOOC mentors. They joined us following the EPIP 7 conference<fnref n="12" />, demonstrating the important function which conferences can have. We also greatly benefited from exchanges with colleagues, in particular Pavel Trofimovich of Concordia University. We are grateful to two anonymous reviewers for their insights; these have significantly improved the paper. We would also like to thank all learners in the MOOC for their valuable input from which we have learnt so much. The authors acknowledge support from VUvereniging [grant AB/rk/2020/088]. We are also indebted to the editors of this volume for their critical eye and constructive input. Any remaining inadequacies in the paper are our own.

## References

Auer, P., Barden, B., & Grosskopf, B. (1998). Subjective and objective parameters determining ‘salience’ in long-term dialect accommodation. *Journal of Sociolinguistics* 2(2), 163–187.
https://doi.org/10.1111/1467-9481.00039

Beinhoff, B. (2013). *Perceiving identity through accent: Attitudes towards non-native speakers and their accents in English*. Peter Lang.

Crystal, D. (2017). *Making sense: The glamorous story of English grammar*. Oxford University Press.

Derwing, T. M., & Munro, M. J. (2009). Putting accent in its place: Rethinking obstacles to communication. *Language Teaching*, 42(4), 476–490.
https://doi.org/10.1017/S026144480800551X

Derwing, T. M. & Munro, M. J. (2015). *Pronunciation fundamentals: Evidence-based perspective for L2-teaching and research*. John Benjamins.

Jenkins, J. (2000). *The phonology of English as an international language: New models, new norms, new goals*. Oxford University Press.

Henderson, A. (2008). Towards intelligibility: Designing short pronunciation courses for advanced field experts. *ASP, 53–54*, 1–22. https://doi.org/10.4000/asp.369

Jansen, S. (2014). Salience effects in the North-West of England. *Linguistik Online*, 66(4), 91–110.
http://dx.doi.org/10.13092/lo.66.1574

Kamps, A. (2021). *Salience of pronunciation features in Spanish L2 learners of English*. [Unpublished Honours project]. Vrije Universiteit Amsterdam.

Labov, W. (1982). Objectivity and subjectivity in linguistic science. *Language in Society*, 11(2), 165–201.

Lev-Ari, S. (2021). Here’s why people might discriminate against foreign accent: New research. *The Conversation*. https://theconversation.com/heres-why-people-might-discriminate-against-foreign-acents-new-research-172539

<footnote n="12">¹² https://epip7.sciencesconf.org/</footnote>
Levis, J. M. (2005). Changing contexts and shifting paradigms in pronunciation teaching. *TESOL Quarterly*, *39*(3), 369–377.

Martin, Von J.-P. & Oebel, G. (2007). Lernen durch lehren: Paradigmenwechsel in der Didaktik. *Deutschunterricht in Japan*, *12*, 4–12.

Ramdas, S. K. K., Slootman, M. W., & Van Oudenhoven-van der Zee, K. (2019). *The VU mixed classroom educational model*. Mixed Classroom Booklet. https://assets.vu.nl/d8b6f1f5-816c-005b-1dc1-e363dd7ce9a5/d7847606-cfa2-482b-8cdec6e7b1bb7e49/Mixed_Classroom_booklet_tcm270-935874.pdf

Ryan, R. M., & Deci, E.L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development and well-being. *American Psychologist*, *55*(1), 68–78. https://doi.org/10.1037//0003-066X.55.1.68

Rupp, L. (2019). *English pronunciation in a global world* [MOOC]. FutureLearn. https://www.futurelearn.com/courses/english-pronunciation.

Philipson, R. (1992). *Linguistic imperialism*. Oxford University Press.

Trudgill, P. (1986). *Dialects in contact*. Blackwell.

## About the authors

**Laura Rupp** is a Senior Lecturer in English Language and Linguistics and director of the Centre for Global English at Vrije Universiteit Amsterdam (VU), The Netherlands. At VU, she teaches, amongst other subjects, Global English and English Pronunciation. She created the online course MOOC English Pronunciation in a Global World in order to address pressing issues that arise from the use of English as a Lingua Franca: both linguistic (the need for mutual understanding in English) and social (accent discrimination and inequality/non-inclusion from lack of access to (good quality) English education). Her research has also focused on grammatical and discourse-pragmatic variation in traditional dialects of English; she has published various articles and co-authored a monograph in this field.

Email: l.m.rupp@vu.nl

**Amrita Das** is the Programme Manager of Didactics & Communication at VU's Centre for Teaching & Learning, overseeing the spectrum of teacher professionalisation, from early career development to senior management and leadership. She worked on the MOOC as a mentor and coordinator, facilitating several runs and helping learners. She is the co-coordinator of Vrije Universiteit's University Teaching Qualification programme and specialises in the VU's Mixed Classroom Educational Model. She has used the Model to understand better the aspects of inclusion and belonging in the MOOC, and wishes to extend its uses across curriculum reflections and other educational practices. Her extensive academic background includes a doctorate in cultural mobility and transnational poetry, specialising in the American ghazal.

**Alisah Kamps** is a Masters student of sociolinguistics at the Universiteit van Amsterdam, The Netherlands. She completed her undergraduate degree in Communication & Information Studies at Vrije Universiteit Amsterdam (VU) with a specialisation in English Language and Linguistics. Alisah has been part of the team of the MOOC English Pronunciation in a Global World since 2020 after she conducted her honours programme project on the saliency of pronunciation features using MOOC data. Currently, she is an internal mentor whose role is to guide learners in the MOOC. After co-presenting the MOOC at EPIP7, Alisah has taken up a new research project that probes the deployment of VU educational Mixed Classroom methods for informing the linguistic attitudes of learners in the MOOC.
**Ericka Acosta** completed a BA degree in applied linguistics from the Communication & Information Studies programme at Vrije Universiteit Amsterdam (VU), The Netherlands. Her specialisation is in language education, and she has a combined minor in neuro studies and digital humanities. In the MOOC English Pronunciation in a Global World, Ericka serves as an internal mentor and a research assistant. As an internal mentor, Ericka supports learners and addresses their questions and concerns. In her role as a research assistant, she organises and enriches MOOC data and she assist in managing social media accounts. Ericka was part of the team that presented the MOOC at EPIP7 in Grenoble. Ericka is to pursue an MA programme in Text Mining at VU.
Sanvicente, F., Ferragne, E., Navarro, S., & Guyot Talbot, A. (2023). French learners of English struggle to identify nasalised American vowels in CVN words. In A. Henderson & A. Kirkova-Naskova (Eds.), *Proceedings of the 7th International Conference on English Pronunciation: Issues and Practices* (pp. 236–248). Université Grenoble-Alpes. https://doi.org/10.5281/zenodo.8225140

# French learners of English struggle to identify nasalised American vowels in CVN words

Francesca Sanvicente

Paris-Cité University and ENS Paris-Saclay

Emmanuel Ferragne

Paris-Cité University

Sylvain Navarro

Paris-Cité University

Anne Guyot Talbot

Paris-Cité University

English and French have different phonological systems (Roach, 2009; Walker, 2001) and French learners have difficulties perceiving some contrasts, such as the approximants /w/ – /r/ (Hallé et al., 1999). They also tend to assimilate multiple English vowels to a single L1 category (Iverson & Evans, 2007). General American exhibits a raising of /æ/ in nasal environments (Carignan et al., 2016), thus creating an acoustic proximity between /æ/ and /ɛ/. The purpose of the present study was to assess the influence of CVN contexts on the perception of vowels by French university students.

We addressed this issue as part of a pedagogical project called SEPALE, which consists of identification and discrimination exercises involving English phonemic contrasts and within which we explored the issue of nasalisation. Thirteen Californian speakers were recorded reading CVC and CVN words, to create stimuli used by undergraduate students in discrimination and perception tasks. Their answers were recorded in logfile, used in our analysis.

We found a higher proportion of errors when /æ/ – /ɛ/ and /ɑ/ – /ʌ/ were in a pre-nasal position and the first contrast yielded better identification and discrimination rates than /ɑ/ – /ʌ/. This finding could inform teachers’ choices of perception or production exercises.

**Keywords:** French learners, nasalisation, identification tasks, perception, discrimination

<figure><img src="image_80.png" /></figure>

This chapter is based on the poster presented by the authors at the 7th International Conference English Pronunciation: Issues and Practices (EPIP 7) held May 18–20, 2022 at Université Grenoble-Alpes, France. It is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of the license, please go to: http://creativecommons.org/licenses/by/4.0/.
# 1 Introduction

General American (hereafter GA) vowels can be affected by nasalisation when followed by a nasal consonant. Thus, we decided to examine the possible perception impairment that they could provoke in French learners. Before /m/, /n/, and /ŋ/, the tongue follows a rising trajectory whose peak differs depending on the consonant; the velar differs from the other two, but for all three a lowering of the first formant of low vowels is generally observed. The alteration of the perceived height of low vowels when followed by a nasal (Mielke et al., 2017) begs the larger question of how a nasal consonant influences the perception of vowels by French learners of English.

SEPALE, a pedagogical project, was used as a platform to look into the possible impact of a CVN (consonant – vowel – nasal) environment on the perception of GA vowels by French learners. SEPALE<fnref n="1" /> is a project consisting of exercises to train the perception of those vocalic contrasts which are supposed to be difficult for French learners of English. The project is part of the participant’s curriculum, so the exercises were completed weekly over a period of 12 weeks, although some students were less assiduous. The results analysed in the present study are the uploaded answers to the perception tasks as done by the participants within the wider SEPALE project.

# 2 Previous research on the topic

There is a large body of academic work pertaining to vowel nasalisation in English. Several authors have notably shown that nasalisation may affect perceived vowel height (e.g., Beddor, 2007; Krakow et al., 1988; Wright, 1975) because of the resulting acoustic shift in the F1 region. Generally speaking, nasalisation entails a lowering of high and mid-vowels and raising of low vowels (Wright, 1975, 1986). Studies combining acoustics and ultrasound tongue imaging have demonstrated that depending on the vowel, tongue height adjustments may be used to compensate or enhance the acoustic effect of nasality. For instance, Carignan et al. (2011) showed that /i/ is produced with a compensatory higher tongue position in pre-nasal context in order to counteract the resulting raising of F1 and to avoid neutralisation with neighbouring /i/. They observed no such adjustment in pre-nasal /ɑ/, where the perceived variation in vowel height induced by nasalisation is acceptable without any risk of misidentifying the vowel. Conversely, Mielke et al. (2017) found that the tongue raising movement of /æ/ in pre-nasal environments may constitute a strategy to enhance the acoustic effect of nasalisation. Carignan et al. (2016) explain that while tongue raising before velar consonants /g/ and /ŋ/ is described as the result of coarticulation (as evidenced by a gradual raising of the tongue in anticipation of the velar constriction), such raising before other nasals peaks around the midpoint of the vowel, indicating “a distinct phonetic target” (p. 316).

Most accents of North American English, including GA, exhibit a raising of /æ/ in pre-nasal environments (Carignan et al., 2016; Labov et al., 2006), the realisation of which is generally described as involving a raising-falling trajectory before /m/ and /n/ (e.g., *pan* [peən]) and a raising trajectory before /ŋ/ (e.g., *bang* [beŋ]). As shown by Krakow et al. (1988), the discrimination of nasalised /ɛ/ and /æ/ in a contextual nasal environment (i.e., when the nasalised nucleus is followed by a nasal consonant) is unproblematic for native speakers. However, the acoustic proximity of the nasalised nuclei in e.g., *bend* and *band* might make it difficult for French learners to distinguish /ɛ/ vs. /æ/ in those environments. This difficulty

<footnote n="1">¹ SEPALE is a project that was developed at Université Paris Cité thanks to funding devoted to pedagogical innovation received from 2021 to 2022.</footnote>
usually bears no equivalence with British varieties where /æ/-raising is largely absent (Mielke et al., 2017).

Given the absence of compensatory tongue adjustment to the nasality induced raising of /ɑ/ (Carignan et al., 2011) and lowering of mid-vowels in pre-nasal environments (Wright, 1975), we also observe difficulties in French learners to correctly discriminate pre-nasal /ɑ/ and /ʌ/ (e.g., *pond* vs. *punned*).

We hypothesised that French learners of L2 English would have more difficulties in perceiving the /æ/ – /ɛ/ and /ɑ/ – /ʌ/ contrasts in pre-nasal environments (e.g., *sand* – *send* and *pond* – *punned*) than in other pre-consonantal environments (e.g., *bat* – *bet* and *cot* – *cut*).

# 3 Methodology

## 3.1 Participants

In this study, 118 second-year students from Université Paris Cité participated in the experiment during the second semester of the academic year. The students were between 19 and 21 years old and their proficiency was at or near a B2 level of English, which is the required level for admission to the *Licence LLCER Anglais* degree programme in French universities.

## 3.2 Stimuli creation

As the purpose of the discrimination study was to determine whether the nasal after the nucleus had an influence on the students' perception of the vowel, a list of both CVC (consonant – vowel – consonant) and CVN one-syllable words was established. The words included the /æ, ɛ, α, λ, ι, i, ɔ/ vowels as a nucleus. Some items with /ɪ/ and /i/ from the final list of stimuli were adapted from Krzonowski (2020), and other CVC and CVN words were added with /æ, ɛ, α, λ, ɔ/. We will focus on the /æ/ – /ɛ/ and /ɑ/ – /ʌ/ contrasts, but the /ɪ/ – /i/ contrast will be included as a baseline because no such nasalisation effect is known to impact its realisation.

We avoided the postvocalic liquid /l/ as mergers have been observed in this context, for example, *pull – pool – pole* may all sound alike due to /l/ (Arnold, 2015), and similarly in *feel – fill* (Labov et al., 2006), where the distinction between /i/ and /ɪ/ is often neutralised for the same reason. We also decided against items with a pre-velar context, such as *pag – peg*. Although there is usually no /æ/-raising before a velar stop in GA, the existence of a raising pattern with a rising tongue trajectory before /g/ has already been observed. As a consequence, in some regions of North America, the word *bag* is pronounced [bejg] (Mielke et al., 2017). The list of stimuli is provided in the Appendix.

Thirteen native speakers of GA (seven women) were recruited in Southern California, all but three of whom were from California. Of those three, one stated that they were from all over the US, another from New Orleans, and someone else from New Jersey. Their ages ranged from 20 to 73 years of age, the average age being 40. The calculated standard deviation was 16, and the median value was 39.

Recordings were made using ROCme! (Ferragne et al., 2012), a program fed with an HTML file to prompt words on a computer's screen. The speakers' productions were recorded with an AT2020 USB microphone. After each item, the speakers were invited to press a gamepad button to switch to the next word. The stimuli were presented in a randomised order to avoid redundancy and fatigue. In total, they each read 161 words, twice. We used both recordings when usable; some were trimmed, others were removed when the background noise was too distracting, or when the beginning of the recording was too close to the onset.
## 3.3 Procedure

Four types of tasks were included in the experiment. Each exercise was divided into sub-types which corresponded to the vowels involved, or the pre-nasal position of the vowel. In this way, students could choose the sub-type they wanted to focus on, and each attempt was composed of 30 prompts selected randomly out of the 161 words recorded by the 13 speakers.

The alternative forced choice exercises 2AFC and 5AFC played one word and provided the participants with two or five boxes, respectively, from which to pick a match for the vowel heard in the word played. For example, in the 5AFC exercise, participants heard a one-syllable word, and had to determine which vowel corresponded to the nucleus. Five options were provided in the form of boxes, as shown in Figure 1. The purpose of these tasks was to test the participant's ability to identify the vowels involved.

<caption>Figure 1</caption>

5AFC Window

<figure><img src="image_81.png" /></figure>

In the AX exercises, participants heard two stimuli and had to determine if they were the same words or if they were different. In this task, two boxes labelled “Same” and “Different” were shown on the screen, and participants had to choose the option corresponding to their answer. This exercise evaluated their ability to differentiate vowel sounds in two minimal pairs. Finally, in the Oddity exercises, three stimuli were heard and the odd one out had to be found. Thus, there were three boxes carrying a number and corresponding to each stimulus. In this last task, their capacity to discriminate sounds was tested.

Participants could hit the “REPLAY” button as many times as they wished. At the end of each attempt, they received feedback in the form of a pop-up window, indicating: the number of wrong and right answers, the total time spent doing the exercise, and the number of times the “REPLAY” button was pressed. Answers were recorded in the logfile created at the end of each attempt, as well as the number of errors and the response times.
## 4 Data analysis and results

Logfiles were analysed with SQLite<fnref n="2" /> (Gaffney et al., 2022) and Python<fnref n="3" /> functions. Table 1 presents the error percentage per type of exercise throughout the study. The exercise type Oddity has the lowest percentage of errors, with AX at the other side of the spectrum.

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

*Percentage of Errors per Type of Exercise*

<table><thead><tr><td>Type of exercise</td><td>Error percentage (%)</td></tr></thead><tbody><tr><td>Oddity</td><td>12.38</td></tr><tr><td>2AFC</td><td>19.83</td></tr><tr><td>5AFC</td><td>23.19</td></tr><tr><td>AX</td><td>25.49</td></tr></tbody></table>

The same observation emerges from Table 2, which shows the percentage of errors in both a non pre-nasal and in a pre-nasal context for the different types of exercises. The lowest percentage of errors in both contexts was found in the Oddity type of exercise, and the highest for CVN was recorded in AX exercises. The impact of nasalisation is shown in the last column, where the subtraction of the CVN error percentage from the CVC's is given. For each of the 118 participants, the mean percentage of errors across all exercises was computed for CVC and CVN contexts separately. In order to determine whether performance was, as expected, poorer in the CVN context, we ran a paired Wilcoxon signed rank test with continuity correction. The test corroborated our hypothesis, as it showed a significant value (V = 232, p < 0.0001).

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

*Percentage of Errors per Type of Exercise in a CVC and CVN Context*

<table><thead><tr><td>Type</td><td>Error percentage when CVC (%)</td><td>Error percentage when CVN (%)</td><td>Difference (%CVN - %CVC)</td></tr></thead><tbody><tr><td>Oddity</td><td>10.59</td><td>16.46</td><td>5.87</td></tr><tr><td>5AFC</td><td>19.09</td><td>26.78</td><td>7.69</td></tr><tr><td>2AFC</td><td>14.93</td><td>24.99</td><td>10.06</td></tr><tr><td>AX</td><td>18.79</td><td>31.10</td><td>12.31</td></tr></tbody></table>

<footnote n="2">2 SQLite (Version 3.31.1) [Computer software]. SQLite Development Team. Retrieved November 11, 2022. Available at https://www.sqlite.org/download.html</footnote>

<footnote n="3">3 Python Software Foundation. Python Language Reference, version 2.7. Available at https://www.python.org/downloads/release/python-3810/</footnote>
Table 3 displays the error percentage for the vowels involved in the study in CVC and CVN contexts. The percentage of errors increases for every item, except for /i/ which was inversely affected by its pre-nasal position, since students actually performed better when the vowel was nasalised. On the other hand, /ʌ/ appears to be the most affected vowel, with an increase in misperceptions of 17.82% when followed by a nasal; /æ/ was also quite affected (16.72%), as was /ɪ/ (15.21%).

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

*Percentage of Errors per Vowel in CVC and CVN Contexts and Impact of Nasalisation (Last Column) in 2AFC and 5AFC Exercises*

<table><thead><tr><th>Vowel</th><th>Error percentage when CVC (%)</th><th>Error percentage when CVN (%)</th><th>Difference (%CVN - %CVC)</th></tr></thead><tbody><tr><td>/i/</td><td>25.33</td><td>9.92</td><td>-15.41</td></tr><tr><td>/ɑ/</td><td>24.80</td><td>28.19</td><td>3.39</td></tr><tr><td>/ε/</td><td>15.47</td><td>29.49</td><td>14.02</td></tr><tr><td>/ɪ/</td><td>15.10</td><td>30.31</td><td>15.21</td></tr><tr><td>/æ/</td><td>6.26</td><td>22.98</td><td>16.72</td></tr><tr><td>/ʌ/</td><td>12.63</td><td>30.45</td><td>17.82</td></tr></tbody></table>

We then focused on the discrimination (AX) exercises, as they naturally lend themselves to the computation of the relatively unbiased d' sensitivity index from Signal Detection Theory (Green & Swets, 1966). A mixed-effects model was fitted to the data in R software with the lmer function from the lmerTest package, with d' as a dependent variable. We added Context and Vowel as fixed effects, as well as the interaction between Context and Vowel. We included Participant as a random effect. Both factors and their interaction were significant (p < 0.0001). Post-hoc comparisons were performed with the emmeans<fnref n="4" /> package and Figure 2 shows these findings. Three distinct groups appear: 1) the /æ/ – /ε/ contrast, for which students performed better when the vowel was not in a pre-nasal position; 2) /ɑ/ – /ʌ/, which showed moderate results in a CVC environment and poor results when the vowels were followed by a nasal; and 3) /ɪ/ – /i/, which obtained good results in both CVC and CVN contexts.

<footnote n="4">⁴ https://cran.r-project.org/web/packages/emmeans/index.html</footnote>
Figure 2

*Sensitivity Indices (d') for Each Combination of the Factors Vowel and Context in the AX Experiments.*

<figure><img src="image_82.png" /></figure>

*Note.* Higher *d*' values show better discrimination. Blue bars show 95% confidence intervals: non-overlapping bars reflect significant differences.

Figures 3 and 4 display the average rate of errors for the /ɑ/ – /ʌ/ and /æ/ – /ɛ/ contrasts. The dotted line corresponds to the linear regression and shows a subtle decreasing trend. On average, students seem to have made more mistakes when the vowel was in a pre-nasal position. The percentage of errors for these pairs followed by a nasal approaches the 0.5 line, while in a non-nasal environment the students seem to have performed better, with a lower average rate of errors for each contrast. Moreover, the line corresponding to the CVN environment never overlaps with the other, the two trends remain almost parallel throughout the period.
<caption>Figure 3</caption>

Average Rate of Errors During the Second Semester of 2022 for the /ɑ/ – /ʌ/ Contrast

<figure><img src="image_83.png" /><figcaption>Figure 4</figcaption></figure>

Average Rate of Errors During the Second Semester of 2022 for the /æ/ – /ɛ/ Contrast

<figure><img src="image_84.png" /></figure>

By way of comparison, Figure 5 shows the average rate of errors through the course of the semester for the /ɪ/ – /i/ vowels, for which the trend of the linear regression is also negative, therefore indicating a slight decrease. The lines corresponding to the CVC and CVN environments overlap, and the average rate of errors, independent of the context, never goes above 0.3.
<caption>Figure 5</caption>

Average Rate of Errors During the Second Semester of 2022 for the /ɪ/ – /i/ Contrast

<figure><img src="image_85.png" /></figure>

5 Discussion and implications

In the course of this study, we sought to determine whether the pre-nasal position of /æ/ – /ɛ/
and /ɑ/ – /ʌ/ vowels could have an influence on the perception of said vowel by French learners
of English. Our results show that the presence of a nasal after these vowels has an impact on
the perception of the nucleus by French learners. The data showed an increase in the percentage
of errors for every item, except for /i/, for which the percentage was surprisingly low in a CVN
environment. The tongue height adjustment strategy at play in nasalised /i/ does seem to favour
the identification of /i/, as evidenced by the radical drop in misidentified /i/ tokens in 2AFC
and 5AFC exercises. The other vowels were impacted by their pre-nasal position, ranging from
a 14.02% to a 17.82% increase in the error percentage in a CVN context for /ɛ, ɪ, æ, ʌ/. In
contrast, /ɑ/ shows relative stability but yields poor performance even in a CVC context.

The influence of nasalisation is especially visible for the /æ/-/ɛ/ and /ɑ/-/ʌ/ vowels. The
results demonstrated that these pairs were better recognised in a non-nasal environment than
when followed by a nasal. Similarly, the /æ/-/ɛ/ contrast seems to have been less difficult to
recognise than the other pair, since it accounted for fewer errors than the /ɑ/-/ʌ/ pair whether
in a CVC or a CVN environment.

When we compare these results to the average rate of errors for the /ɪ/ – /i/ contrast, we find
a clear difference between this contrast and the other two. The superimposition of the two lines
corresponding to the CVC and CVN environments indicates that in the case of this pair, vowels
do not seem to be affected by their pre-nasal position. This comparison clearly suggests that
nasalisation has a major impact on the /æ/ – /ɛ/ and /ɑ/ – /ʌ/ contrasts, supporting the findings
of previous studies. In fact, the raising-falling trajectory of /æ/ in pre-nasal environments
mentioned by Carignan et al. (2016) and Labov et al. (2006) brings the vowel closer to the
realisation of /ɛ/, making the distinction between both sounds more difficult for French learners.
Similarly, our findings concur with the observation of a lowering of mid-vowels (such as /ʌ/)
in pre-nasal positions (Wright, 1975), and with the absence of compensatory tongue adjustment
to the raising of nasalised /ɑ/ (Carignan et al., 2011), since participants performed less well
when the vowels were followed by a nasal.

In terms of pedagogical implications, these findings could be used to assess the extent to
which example word lists or minimal pairs in textbooks are appropriate for French learners of
English. Teachers could then sequence lexical items in relation to their CVC or CVN context,
or at least be aware that the CVN context may prove more challenging for learners to perceive.
For example, in the first section of Hancock (2012), the vowel sound /æ/ is explored through different words. A note draws together words such as *ask* and *dance*, and explains that the vowel is generally produced with /æ/. In addition, it is stated that the sound may be /ɑ:/ in South-East England. In this case, a supplementary note could have indicated that in GA, the vowel sound in a pre-nasal position is closer to what Carignan et al. (2016) transcribe as [eə]. This would raise awareness about the dialectal differences in the pronunciation of /æ/ vowels in a CVN context.

## 6 Conclusion

The purpose of this paper was to analyse the influence of nasalisation on the perception of GA vowels by French undergraduates studying English. SQL queries, tables and plots were used to isolate tendencies in the effects of a nasal after a vowel, revealing a greater impact on the /æ/ – /ɛ/ and /ɑ/ – /ʌ/ vowels. Our paper signals the importance of drawing the attention of teachers to the influence of nasalisation on GA vowels and to the way they are perceived by French learners, since we have shown that nasalised /æ/ – /ɛ/ and /ɑ/ – /ʌ/ vowels are more difficult to discriminate.

A potential improvement to our study would be to consider the effect of the speaker's gender on vowel perception. Krzonowski et al. (2018) highlighted, in their acoustic study, a difference in the production of vowels by female and male English native speakers. As an example, a slight overlap was observed in the production of the /ɪ/ – /i/ vowels when produced by English female speakers, and this was exacerbated when pronounced by male speakers. It may therefore be the case that the difference between /ɪ/ – /i/ when produced by male speakers in our SEPALE exercises is harder for our students. We have included the speaker gender in the scripts which create the database, and further SEPALE studies could request that participants indicate their gender prior to doing the exercises. This way, the difference in terms of vocal tract sizes and vowel frequency between women and men could be compared to the success rates in vowel perception.

## Acknowledgements

This study was funded by "Innovations pédagogiques" ANR-18-IDEX-0001 - IdEx Université Paris Cité.

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Appendix

List of Stimuli

<table><thead><tr><th>Context</th><th>/æ/</th><th>/ɛ/</th><th>/ɑ/</th><th>/ʌ/</th><th>/ɔ/</th><th>/ɪ/</th><th>/ɪ/</th></tr></thead><tbody><tr><td>/_p/</td><td>cap</td><td>-</td><td>cop</td><td>cup</td><td>-</td><td>kip</td><td>keep</td></tr><tr><td>/_t/</td><td>cat mat pat tat</td><td>met pet</td><td>bot cot not pot rot tot</td><td>but cut mutt nut putt tut</td><td>bought caught naught taught wrought</td><td>-</td><td>-</td></tr><tr><td>/_k/</td><td>back knack lack pack</td><td>beck neck peck</td><td>bock lock pock stock tock</td><td>buck luck puck</td><td>stalk talk baulk chalk</td><td>bik lick pick</td><td>beak leak peak</td></tr><tr><td>/_d/</td><td>cad dad had mad rad sad</td><td>dead head med red said</td><td>bod cod mod nod sod</td><td>bud cud</td><td>baud gnawed sawed</td><td>did</td><td>deed</td></tr><tr><td>/_m/</td><td>cam ham stamp tam</td><td>hem</td><td>bomb pomp stomp tom</td><td>bum chump come hum pump</td><td>-</td><td>dim</td><td>deem</td></tr><tr><td>/_n/</td><td>ban bland can dan dance land man pan ranch sand tan</td><td>ben dense ken lend men pen send ten wrench</td><td>blonde con don fond non pond</td><td>bun done fund nun pun punned tun</td><td>-</td><td>bin kin min</td><td>bean keen mean</td></tr><tr><td>/_ŋ/</td><td>-</td><td>-</td><td>bong honk long</td><td>bung hunk lung</td><td>-</td><td>-</td><td>-</td></tr><tr><td>/_f/</td><td>-</td><td>-</td><td>cough</td><td>cuff</td><td>-</td><td>-</td><td>-</td></tr><tr><td>/_s/</td><td>-</td><td>-</td><td>-</td><td>crust</td><td>crossed</td><td>-</td><td>-</td></tr><tr><td>/_r/</td><td>-</td><td>-</td><td>bar bard card farm par parch spar star</td><td>-</td><td>bore bored form porch pore spore store tort</td><td>-</td><td>-</td></tr></tbody></table>
**About the authors**

**Francesca Sanvicente** is a Master’s degree student at Université Paris Cité and ENS Paris-Saclay

Email: francesca.sanvicente@ens-paris-saclay.fr

**Emmanuel Ferragne** is a Professor of English phonetics and has been at Université Paris Cité since 2009. His current research is concerned with sociophonetics, the acquisition of English phonology by French learners, and forensic voice comparison.

Email: emmanuel.ferragne@u-paris.fr

**Sylvain Navarro** is an Associate Professor of English phonology and has been at Université Paris Cité since 2015. He coordinates the PAC (*Phonologie de l'Anglais Contemporain*) research program. His research interests revolve around phonological and phonetic variation in varieties of English, with a particular focus on phenomena relating to rhoticity.

Email: sylvain.navarro@u-paris.fr

**Anne Guyot Talbot** is an Associate Professor of English phonetics and phonology and has been at Université Paris Cité since 2007. Her current research focuses on the link between morphology and lexical stress, lexical stress location, and English intonation.

Email: anne.talbot@u-paris.fr
Thir, V. (2023). Understood or not? Issues in using orthographic transcription for assessing intelligibility to international listeners of English. In A. Henderson & A. Kirkova-Naskova (Eds.), *Proceedings of the 7th International Conference on English Pronunciation: Issues and Practices* (pp. 249–260). Université Grenoble-Alpes. https://doi.org/10.5281/zenodo.8225289

# Understood or not? Issues in using orthographic transcription for assessing intelligibility to international listeners of English

Veronika Thir

University of Passau

The most popular method for measuring intelligibility of L2 pronunciation is orthographic transcription (Munro & Derwing, 2015). This paper discusses the construct of intelligibility underlying this method, as well as its strengths and weaknesses compared to other methods of assessing intelligibility, such as shadowing. It highlights issues arising when using orthographic transcription with large, linguistically diverse samples of listeners at different proficiency levels, as is often the case in English as a lingua franca (ELF) research. This is not to say that orthographic transcription should not be used for ELF intelligibility studies, in particular quantitative ones. Instead, a case is made for carefully considering its potential biases in particular research contexts, so that appropriate measures to counteract them can be taken to ensure a satisfactory level of validity and reliability. This argument is illustrated with examples from a large-scale study (N = 508) involving both native and non-native listeners from over 80 different L1 backgrounds at different proficiency levels.

**Keywords:** intelligibility, orthographic transcription, research methods, English as an international language (EIL), English as a lingua franca (ELF)

<figure><img src="image_86.png" /></figure>

This chapter is based on the oral presentation given by the author at the 7th International Conference English Pronunciation: Issues and Practices (EPIP 7) held May 18–20, 2022 at Université Grenoble-Alpes, France. It is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of the license, please go to: http://creativecommons.org/licenses/by/4.0/.
**1 Introduction**

Since intelligibility is frequently named as the primary goal of L2 pronunciation teaching, its
assessment has particular relevance to researchers and practitioners in L2 pronunciation
pedagogy. However, since it is a perceptual phenomenon, its measurement constitutes a
challenge. Various methods for assessing intelligibility have been proposed, such as
comprehension questions, responses to true/false statements, listener summaries, scalar ratings,
shadowing or even focused interviews (see Kang et al., 2018; Munro & Derwing, 2015). The
most popular one in L2 pronunciation research is orthographic transcription (Munro &
Derwing, 2015), whereby subjects listen to an audio stimulus and write down what they hear.
The number of correctly transcribed words (which may be minimal pairs, key words or entire
sentences, depending on the research questions) is taken to reflect the amount of understanding
that has taken place.

This paper takes a closer look at orthographic transcription by examining the construct of
intelligibility underlying it, as well as its validity and reliability in comparison to other
widespread methods of measuring intelligibility, in particular shadowing. In doing so, it
scrutinises two approaches to coding orthographic transcriptions: the ‘exact word match’
technique and the allowance of spelling errors. The latter seems preferable with non-native
listeners, who may be penalised when using a strict exact word match. However, if spelling
errors are accepted, it may sometimes be difficult to distinguish them from transcriptions
reflecting unintelligibility. Such ‘ambiguous’ transcriptions become more frequent when
working with large, linguistically heterogenous listener samples, as is the case in quantitative
studies on international intelligibility, i.e., intelligibility among English as a lingua franca
(ELF) users. The present paper highlights issues and challenges in this respect by reporting on
the process of data coding in an ELF intelligibility study which involved over 500 native and
non-native listeners from a wide variety of L1 backgrounds at different proficiency levels. It is
argued that neither coding technique is inherently superior, but that careful consideration of the
sample and research context is necessary before deciding on a technique, which also needs to
be considered in the interpretation of results.

**2 What type of intelligibility are we measuring with orthographic transcription?**

Intelligibility is a complex concept that is defined in various ways within and across different
fields (e.g., Derwing & Munro, 1997; Schiavetti, 1992) and different methods of assessing
intelligibility might more validly reflect certain intelligibility constructs than others. When
listeners transcribe the words they hear, they are essentially engaging in the process of word
identification. This most closely resembles Smith’s (1992) definition of *intelligibility* as
“word/utterance recognition” (p. 76) in his framework; the other two dimensions are
*comprehensibility* (i.e., understanding the literal meaning of a word or utterance) and
*interpretability* (i.e., understanding the pragmatic meaning behind a word or utterance). One
weakness of this framework, however, is its implicit bottom-up view of the comprehension
process: the three components are regarded as “degrees of understanding on a continuum, with
intelligibility being lowest and interpretability being highest” (Smith, 1992, p. 76). This leads
to issues in empirical operationalisation, since

the comprehension of speech is not a linear process: when a word cannot be recognized
on the basis of bottom-up cognitive processes, the listener may nonetheless be able to
‘fill it in’ by exploiting top-down strategies. (Munro & Derwing, 2015, p. 378)
Thus, orthographic transcription “reflects more than just low-level speech processing” (Munro & Derwing, 2015, p. 378), i.e., it measures more than intelligibility as defined by Smith. Zielinski (2012) also points to “the highly interdependent nature of the different components of the Smith framework” (p. 405) and, likewise, to difficulties in empirical application:

[...] researchers might find it difficult to measure intelligibility as separate from comprehensibility because listeners will use what is a natural process of listening and refer to the meaning and context of an utterance to identify difficult words within it. (Zielinski, 2012, p. 405)

There is abundant empirical evidence supporting the use of context in the process of spoken word recognition (see e.g., Bent et al., 2019; Kennedy & Trofimovich, 2008; Thir 2020a, 2020b). Thus, if we interpret the components in Smith’s framework as interrelated rather than hierarchical, this more accurately reflects the nature of speech comprehension. Intelligibility defined as spoken word recognition is therefore best understood as a cognitively interactive process that involves going back and forth between bottom-up and top-down processing (see also Mirman, 2017). Following Magnuson (2017), intelligibility is defined here as the process of “[...] map[ping] phonological forms to intended words in memory” (p. 76). With these adjustments, orthographic transcription seems well suited to measuring intelligibility as spoken word recognition (henceforth SWR). It also seems to meet quality criteria in academic research, such as validity and reliability.

## 3 Validity and reliability of orthographic transcription in measuring intelligibility as SWR

Validity, here mainly understood as construct validity (e.g., Gass, 2015), refers to the quality of accurately capturing the phenomenon under investigation: “a test is valid if it measures what it is supposed to measure” (Dörnyei, 2007, p. 51). One major advantage of orthographic transcription is that it allows SWR to be quantified more accurately than, for instance, listener ratings of intelligibility. Listener ratings lack validity since they only reflect how much listeners *think* they understood (Munro & Derwing, 2015) and risk confounding intelligibility with similar but separate phenomena such as “ease or effort of understanding” (termed comprehensibility in Munro & Derwing, 2015, p. 380). Compared to methods such as comprehension questions, summaries, and sentence verification, orthographic transcription allows for a more precise focus on the phenomenon of SWR. Moreover, it makes it possible to measure the intelligibility of decontextualised words (e.g., by presenting individual words or nonsense sentences), which may be necessary in certain research contexts (see §4).

One might argue that the same is true for shadowing, another common technique to measure SWR. This involves listeners instantly repeating an audio stimulus, with correct repetition being treated as signifying understanding. However, it is debatable whether correct repetition can be regarded as a sign of correct word identification (i.e., of having assigned the auditory input to the intended entry in the mental lexicon), or whether it measures phoneme (or even phone) recognition. Clearly, it is possible to correctly repeat a sound sequence without understanding what it means. At least in the case of English, it is, however, more difficult to correctly transcribe a word without knowing what the sound sequence refers to, so the danger of measuring sound rather than word recognition seems lower in the case of orthographic transcription.

For both methods, a relevant question is how precisely they measure SWR: do they only measure listeners’ ability to identify spoken words, or do other skills factor into them?
Orthographic transcription obviously presupposes some competence in standard orthography, and the extent to which it also measures orthographic skills depends on how potential spelling errors are treated in data analysis. When examining previous research, two approaches emerge in this respect. The exact word match (e.g., Derwing & Munro, 1997; Munro et al., 2006) only accepts perfect matches to standard orthography. The allowance of spelling errors<fnref n="1" /> (e.g., Bradlow & Pisoni, 1999; Field, 2005; Kennedy & Trofimovich, 2008), however, arguably offers greater validity since it does not conflate orthographic skills and SWR. This seems particularly important when working with non-native listeners and other groups prone to orthographic weaknesses, who might be penalised when a strict exact word match is used. However, the allowance of spelling errors does not necessarily meet the criterion of reliability, though this may be counteracted to some extent by using multiple coders when analysing listeners' transcriptions.

Hence, some may be quick to recommend shadowing in lieu of orthographic transcription when working with non-native listeners, to overcome the issue of additionally measuring orthographic skills. However, shadowing presupposes something else which many L2 listeners lack: an ability to articulate target words to a recognisable extent. As L2 pronunciation researchers and practitioners can well attest, numerous non-native listeners may very well correctly identify a word or phrase but may not necessarily be able to pronounce it in a recognisable way themselves. Thus, shadowing may lack validity in that it measures not only SWR, but also pronunciation skills. This is particularly problematic when studying the intelligibility of minimal pair words, which L2 listeners might be able to distinguish perceptively, but at the same time might not be able to distinguish productively. A similar issue may arise when working with native listeners whose L1 dialect contains different phonemic distinctions than the variety under study. Since orthography is far more standardised than pronunciation, and L2 learners are typically more competent in spelling than pronunciation, especially when it comes to minimal pair words, orthographic transcription seems to offer greater validity in such cases.

The second criterion, reliability, refers to consistency in measurement (see, for example, Dörnyei, 2007). Regarding raters or coders, it may be defined as “degree of consistency with which instances are assigned to the same category by different observers or by the same observer on different occasions” (Hammersley, 1992, p. 67), which refers to inter- and intra-rater reliability respectively. These types of reliability may be improved by using predetermined, precisely defined criteria when coding or rating participant responses. However, depending on the type of responses, a certain amount of personal judgment by the rater may still be necessary, potentially compromising inter- and intra-rater reliability. One major advantage of orthographic transcription is that it typically necessitates less such judgment than other measures of intelligibility that involve a greater amount of open-ended input by respondents, such as summaries or comprehension questions. Shadowing does not fare better either, since, provided no acoustic analysis is performed, it involves the auditory impressions of raters. Orthographic transcription is superior in this respect especially when using the exact word match technique; comparing participant entries to standard orthography is a straightforward and unambiguous approach ensuring high inter-rater and intra-rater reliability.

With the allowance of spelling errors, however, some variation in raters' judgments is to be expected. The extent to which inter-rater reliability may thus be compromised depends on

<footnote n="1">¹ The distinction between these approaches is not entirely clear cut. For example, Bent and Bradlow (2003) measured intelligibility by “counting the number of keywords transcribed perfectly” (p. 1605), with missing or superfluous morphemes resulting in words being coded as incorrect. However, “obvious spelling errors were not counted as incorrect” (p. 1605). Similarly, Derwing et al. (2002) describe their coding scheme as “an exact word match technique”, but with the proviso that they “ignore[d] spelling errors” (p. 252).</footnote>
factors such as the amount of orthographic variation in the data and the proficiency level of
listeners. With moderately sized samples of educated native speaker listeners or highly
proficient L2 listeners, such variation is typically comparatively minor, and inter-rater
reliability is unlikely to be unacceptably low. A different picture emerges when investigating
large, linguistically heterogenous samples of L2 listeners at different proficiency levels, who
may all bring their own L1-specific orthographic weaknesses with them. This is particularly
relevant to the ever-growing field of ELF intelligibility research, which, by definition, aims at
studying intelligibility for internationally diverse populations consisting mostly of L2 listeners.
In such cases, researchers may face several challenges when coding orthographic
transcriptions.

4 Problems with the allowance of spelling errors: A large-scale ELF intelligibility study

Before discussing the issues that arose in the process of coding orthographic transcriptions in
my own study (see §4.2), we need to consider the research context, i.e., the study's aim and
underlying hypothesis, the exact method adopted, and the nature of the sample of listeners (see
§4.1). This is necessary to comprehend the significance and potential implications of the issues
arising in the coding process for the study's findings.

**4.1 Research context**

The study investigated the effect of co-text and context on the international intelligibility of two features of an Austrian accent in English (for a rationale, see Thir, 2020a, 2020b). These were the realisation of TRAP /æ/ as [e] (thus conflating the TRAP – DRESS distinction) and the realisation of NURSE /ɜ:/ as [øə].<fnref n="2" /> A subsidiary aim of the study was to investigate the effect on intelligibility of these two sound substitutions (Thir, 2020a), as well as differences in intelligibility between mono- and disyllabic words, since longer words have been found to be more intelligible than shorter words (see e.g., Howes, 1957).

A listening experiment consisting of four different conditions was developed. Three of these involved the presentation of target words in sentence co-text in the form of a cloze test (for details see Thir 2020a, 2020b), while the remaining one was a control condition where words were presented in isolation. In the conditions involving a cloze test, participants saw a single sentence on their screens with a gap in the place of the target word. In the SYN CONDITION, carrier sentences were semantically neutral and merely indicated the part of speech (POS) of the target word (e.g., *It's quite *______*.), i.e., they contained a syntactic cue. In the SYN+SEM CONDITION, in addition to indicating the POS of the target word, carrier sentences also included a semantic cue (e.g., *feather* for the target word *bird* in *They found the feather of a *______*.*). In the SYN+SCH CONDITION, carrier sentences were semantically neutral but an additional schematic cue in the form of a short description under which the statement was made was presented before each sentence (e.g., *At the airport before the carrier sentence I need to pick up my *______*.*, for the target word *bag*).

Intelligibility was measured as orthographic transcription, i.e., after hearing the entire
sentence, participants had to type the target word into the gap on their screens before moving
on to the next sentence. Each condition contained six different types of target words which
were intermixed with nine distractor words, as shown in Table 1:

<footnote n="2">² These are Wells’ (1982, p. 120) standard lexical sets for English vowels: TRAP: /æ/, DRESS: /e/, and NURSE: /ɜ:/, which are used as keywords in the main text.</footnote>
<caption>**Table 1**</caption>

<table><caption>*The 24 Target Words in the Experiment*</caption><thead><tr><th>CONDITION</th><th>NURSE monosyll.</th><th>NURSE disyll.</th><th>TRAP monosyll. non-MP</th><th>TRAP disyll.</th><th>TRAP MP 'different POS'</th><th>TRAP MP 'same POS'</th></tr></thead><tbody><tr><td>C</td><td>birth</td><td>worker</td><td>rat</td><td>palace</td><td>sand (n) – send (v)</td><td>land (v) – lend (v)</td></tr><tr><td>SYN</td><td>nurse</td><td>curtain</td><td>flat</td><td>massive</td><td>bad (adj.) – bed (n)</td><td>gas (n) – guess (n)</td></tr><tr><td>SYN+SEM</td><td>bird</td><td>purple</td><td>van</td><td>chapter</td><td>dad (n) – dead (adj./adv.)</td><td>pan (n) – pen (n)</td></tr><tr><td>SYN+SCH</td><td>firm</td><td>servant</td><td>cab</td><td>servant</td><td>bag (n) – beg (v)</td><td>pants (n pl.) – pence (n pl.)</td></tr></tbody></table>

*Note.* For minimal pair (MP) words, the corresponding DRESS word and the part of speech (POS) is provided where n = noun, v = verb, adj. = adjective, adv. = adverb.

To obtain data from participants who were not necessarily familiar with the Austrian accent, the experiment was conducted via the internet with the help of the survey tool *SoSciSurvey*<fnref n="3" />. Listeners were recruited via e-mail, social media and the author's international contacts. The sample consisted of 508 (M = 175, F = 330, Other = 3) native listeners (NL: *n* = 66; 13%) and non-native listeners of English (NNL: *n* = 442; 87%), aged 18–74 years (M = 29.4). The listeners came from 81 different L1 backgrounds<fnref n="4" />, as summarised in Figure 1. Most had a Romance language as their L1 (*n* = 107; 21%), followed by Slavic languages (*n* = 54; 11%), Turkish (*n* = 52; 10%) and a Germanic language other than English (*n* = 48; 9%).

<footnote n="3">³ SoSciSurvey https://www.soscisurvey.de/</footnote>

<footnote n="4">⁴ Different combinations of two or three first languages were counted as different L1 backgrounds; for a full list, see Thir (2020b).</footnote>
<caption>**Figure 1**</caption>

*Overview of L1 Backgrounds Grouped into Selected Language Families in the Sample*

<figure><img src="image_87.png" /></figure>

Participants were asked to assess their listening proficiency using a slightly adapted version of the self-assessment scale for listening in a foreign language of the *Common European Framework of Reference* (Council of Europe, 2018, p. 167). Table 2 shows that most listeners considered themselves quite advanced (67% combined at C1 or C2 level), some reported being at an intermediate level (29% combined at B1 or B2) and the remaining participants assessed themselves at a beginner level (4% combined at A1 or A2).

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

*Participants' self-assessed listening proficiency*

<table><thead><tr><td>CEFR level</td><td>n</td><td>%</td></tr></thead><tbody><tr><td>A1</td><td>8</td><td>2</td></tr><tr><td>A2</td><td>12</td><td>2</td></tr><tr><td>B1</td><td>52</td><td>10</td></tr><tr><td>B2</td><td>95</td><td>19</td></tr><tr><td>C1</td><td>129</td><td>25</td></tr><tr><td>C2</td><td>212</td><td>42</td></tr><tr><td>Total</td><td>508</td><td></td></tr></tbody></table>
**4.2 Issues in coding orthographic transcriptions**

To increase the validity of the chosen measurement of intelligibility, the option of allowing spelling errors and missing or added morphemes was initially considered in the coding process. As a first step, 217 ambiguous transcriptions were identified, i.e., although they did not perfectly match the dictionary entry of a word, they could potentially be classified as indicative of intelligibility, which amounts to 1.8% of all 12.192 entries. Notably, certain target words exhibited a much higher percentage of such unclear cases than others. Figures 2a and 2b list all target words in descending order according to their proportion of unclear cases. The order is the same in Figure 2a (which compares mono- and disyllabic words) and Figure 2b (which compares NURSE and TRAP words). By far the highest proportion of unclear cases was identified for *servant* (20.9%), followed by *curtain* (6.5%) and *hammer* (5.3%).

**Figures 2a and 2b**

<figure><img src="image_88.png" /><figcaption>Percentage of Unclear Cases for Each Target Word: a) Mono- vs. Disyllabic Words; and b) NURSE vs. TRAP words</figcaption></figure>

As shown in Figures 2a and 2b, there was a tendency for disyllabic words and NURSE words to exhibit a higher number of ambiguous transcriptions. Since differences in intelligibility between mono- and disyllabic words and between NURSE and TRAP words were part of the research questions investigated in Thir (2020a, 2020b), ensuring reliability in coding such cases was crucial to avoid biasing the results. Therefore, all ambiguous transcriptions were presented to eight researchers at the University of Vienna at an ELF research meeting. It soon became clear that inter-rater reliability was compromised, since there was considerable disagreement. For certain words, there was a continuum of ambiguous spellings (e.g., *purple* in Table 3), and it was impossible to decide where to draw the line between acceptable and unacceptable ones. For others, there were fewer, but equally tricky cases. For example, *van* (pronounced as [ven]), was transcribed as <vane> by six listeners. This could simply constitute a misspelling, but could also point to listeners having incorrectly identified the word as /veɪn/, transcribing it in
analogy to *pane*. Considering these circumstances, a strict exact word match approach<fnref n="5" /> was chosen to ensure coding reliability, though this inevitably penalised (some of) the non-native listeners in the sample. Naturally, this needed to be taken into consideration when interpreting the results, especially in relation to differences in performance between native and non-native listeners, or listeners at different proficiency levels.

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

*Ambiguous Spellings for the Word Purple*

<table><thead><tr><td>Spelling</td><td>Frequency</td></tr></thead><tbody><tr><td>purpel</td><td>1</td></tr><tr><td>purpal</td><td>1</td></tr><tr><td>purpul</td><td>1</td></tr><tr><td>purpule</td><td>1</td></tr><tr><td>purpole</td><td>1</td></tr><tr><td>puprle</td><td>1</td></tr><tr><td>perpleo</td><td>1</td></tr><tr><td>perpul</td><td>1</td></tr><tr><td>puple</td><td>1</td></tr><tr><td>pupple</td><td>1</td></tr><tr><td>purper</td><td>2</td></tr><tr><td>perpur</td><td>1</td></tr></tbody></table>

*Note.* The last two options were considered ambiguous since they resembled the German word *Purpur* (signifying *purple*).

There were, however, a few exceptions to this strict approach, which seemed clear cases of intelligibility. These included capitalisations (e.g., *Worker* or *WORKER* for *worker*), insertion of punctuation marks or of a numeral key (e.g., *flat4* for *flat*) and if parts of a carrier sentence had additionally been transcribed (e.g., *a van* for *van*). In one case, a listener had noticed the phonological ambiguity in the stimulus and provided two options (*Land* and *Lent* for *land* in the control condition), which was also accepted.

Another reason why the exact word match seemed most appropriate for this study relates to the issue of objectivity. During the coding process, it seemed more tempting to accept spelling errors in longer (i.e., disyllabic) words, since they were still more easily recognisable as the intended words. For example, it seemed obvious that *messive* was a misspelling of *massive*, while *ret* for *rat* or *lend* for *land* clearly seemed to indicate incorrect SWR, although all cases involved the substitution <e> for <a>. Especially in the case of TRAP minimal pair words, there seemed to be little reason to classify such substitutions as anything other than incorrect word identification. Since differences in intelligibility between mono- and disyllabic words and between minimal pair and non-minimal pair TRAP words were part of the research questions examined, treating the substitution <e> for <a> differently depending on the word type would have biased the results. Thus, there would have been a danger of circularity, or of falling prey to a self-fulfilling prophecy by using a coder's *written* word recognition of the target word as

<footnote n="5">⁵ The choice was made in consultation with Prof. Barbara Seidlhofer, a leading scholar in the field of ELF.</footnote>
a criterion in coding transcriptions. Figure 3 illustrates this issue using the example of the
hypothesis that spoken longer words are more intelligible than shorter ones (caption 1). Partly
influenced by this hypothesis, and partly influenced by their ability to recognise the intended
word more easily in the case of disyllabic (i.e., longer) words (caption 2), the coder will likely
conclude that an ambiguous transcription of a disyllabic word is a manifestation of
orthographic weakness rather than failed SWR (caption 3). Consequently, disyllabic words are
more likely to be classified as correctly identified than monosyllabic words (caption 4), which
then results in the confirmation of the initial hypothesis (caption 1).

**Figure 3**

*Example of Circularity in the Coding Process When Coder's Written Word Recognition Is Used as Criterion for Classifying Transcriptions*

<figure><img src="image_89.png" /></figure>

**5 Discussion and conclusion**

This paper highlighted several advantages of orthographic transcription to measure intelligibility if the construct is defined in terms of SWR, notably over the method of shadowing, which seems problematic when working with non-native listeners. At the same time, it stressed that the validity and reliability of orthographic transcription depends on which approach to coding is adopted: the exact word match or the allowance of spelling errors, with the former offering greater reliability but lower validity than the latter. The present paper highlighted how reliability and objectivity might be compromised when allowing spelling errors while working with large, linguistically heterogeneous samples of listeners at various proficiency levels, as is often the case in quantitative ELF intelligibility studies. Crucially, the
conclusion that should be drawn from this analysis is not that the exact word match technique
is superior per se. Rather, it is necessary to carefully weigh the considerations discussed here
in each particular research context, that is, in relation to the research questions and hypotheses
examined, the nature of the stimuli material (e.g., minimal pair words) and the size and nature
of the sample. Clearly, there will always be a trade-off between validity, reliability and also
feasibility, and the exact nature of this trade-off can only be determined in each specific
research context and needs to be taken into consideration when interpreting the results.

This paper also demonstrated that even in quantitative studies, some issues in data analysis
might only emerge when taking a closer look at one's raw data, such as what participants typed
in as transcriptions. This highlights the importance of poring intently over one's raw data,
especially when working with under-researched populations for whom certain well-established
research methods might turn out to be (somewhat) problematic. International listeners,
especially at intermediate and lower proficiency levels, constitute such a population in
quantitative intelligibility research. However, it is precisely these listeners who need to receive
greater attention due to their ever-increasing use of English for cross-cultural communication
worldwide and the ensuing issues of intelligibility in international contexts. This is not to say
that well-established methods should not be used with such populations, but that certain
adaptations might be necessary to ensure an appropriate balance of validity and reliability.

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## About the author

**Veronika Thir** holds a PhD in English Applied Linguistics from the University of Vienna. Her research interests focus on issues of intelligibility in ELF communication, in particular as they relate to the use of linguistic co-text and extralinguistic context by international listeners. Her work has been published in the *Journal of Second Language Pronunciation* and as a chapter in *Language Change: The Impact of English as a Lingua Franca* (2021, CUP). She is currently working as a post-doctoral researcher at the University of Passau, in a research project on cultural differences regarding personal data disclosure.

Email: Veronika.Thir@uni-passau.de
Xi, X., Li, P. & Prieto, P. (2023). Reducing acoustic overlap of L2 English vowels through gestures encoding lip aperture. In A. Henderson & A. Kirkova-Naskova (Eds.), *Proceedings of the 7th International Conference on English Pronunciation: Issues and Practices* (pp. 261–270). Université Grenoble-Alpes. https://doi.org/10.5281/zenodo.8225191

# Reducing acoustic overlap of L2 English vowels through gestures encoding lip aperture

Xiaotong Xi

Pompeu Fabra University, Barcelona

Peng Li

University of Oslo

Pilar Prieto

Pompeu Fabra University, Barcelona

Catalan Institution for Research and Advanced Studies, Barcelona

This study aims to investigate whether hand gestures mimicking the lip aperture of non-native vowels can improve learners' production accuracy after audiovisual perceptual phonetic training. Sixty-six Catalan/Spanish bilingual learners of English were randomly assigned to either the No Gesture or Gesture group for training on the challenging English vowels /æ/ and /ʌ/. In the Gesture group, participants saw the instructor perform gestures mimicking the lip aperture of the low vowel /æ/ and mid vowel /ʌ/. Participants in the No Gesture group only saw the instructor produce the speech. Pronunciation performance was evaluated before and after training using paragraph reading, picture naming, and word imitation tasks. Pillai scores were used to measure the acoustic overlap between /æ/ and /ʌ/. The results showed that although both training groups showed less overlap between the two vowels after training, gestural training had a greater effect than no gesture training in the picture naming and paragraph reading tasks. These findings suggest that hand gestures mimicking visible articulatory features, such as lip aperture, can improve the pronunciation accuracy of non-native sounds in L2 learners.

**Keywords:** hand gestures, English vowels, L2 production, acoustic overlap, phonetic training

<figure><img src="image_90.png" /></figure>

This chapter is based on the oral presentation given by the authors at the 7th International Conference English Pronunciation: Issues and Practices (EPIP 7) held May 18–20, 2022 at Université Grenoble-Alpes, France. It is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of the license, please go to: http://creativecommons.org/licenses/by/4.0/.
# 1 Introduction

## 1.1 Pronunciation training with multiple modalities

One of the difficulties adults face as they learn an L2 is the pronunciation of non-native sounds. While these difficulties can be attributed to factors such as the transfer from the L1 phonetic realisation to the L2 production (Kartushina & Frauenfelder, 2014), the limited quantity of the L2 input (Muñoz, 2008), and the quality of the L2 pronunciation instruction (Derwing & Munro, 2015), phonetic training can help overcome these difficulties.

According to the Dual Coding Theory (Paivio, 2014), learners may retain and retrieve information coded through verbal and non-verbal channels more easily than through one modality. Focusing on L2 phonological acquisition, audiovisual phonetic training can boost L2 speech sound learning by providing visual information from facial gestures (e.g., Hazan et al., 2005; Inceoglu, 2016). Furthermore, multimodal phonetic training with embodied cues may provide richer visual sources for L2 speech learning. This is supported by the Embodied Cognition theory, which holds that physical actions can shape cognitive processes (Wilson, 2002). Shapiro and Stolz (2019) as well as Sullivan (2018) argue that embodiment offers benefits in educational settings, where the role of hand gestures in learning has received significant attention. Specifically, while teachers' gestures can help lighten learners' cognitive load by shifting the burden from verbal to visuospatial processing, learners' self-performed gestures indicate whether they have fully comprehended the concept being taught (Shapiro & Stolz, 2019). The benefits of hand gestures have been extensively investigated in different learning domains, such as mathematics, science, and languages, among many others (for a review, see Novack & Goldin-Meadow, 2015). Regarding L2 pronunciation, hand gestures mimicking phonetic features have revealed positive effects on L2 speech production. In the following section, we will review the results of studies investigating the effectiveness of hand gestures in training L2 sound pronunciation.

## 1.2 Phonetic training with hand gestures

While hand gestures are frequently used by teachers in L2 classrooms to teach pronunciation (Hudson, 2011; Smotrova, 2017), their effectiveness in the learning of L2 sounds has only been tested in a few empirical studies, with mixed results. Some studies have provided positive results of using hand gestures. For example, horizontal sweep gestures mimicking durational features improved the pronunciation accuracy of L2 Japanese long vowels (Li et al., 2020). Similarly, hand gestures mimicking the air burst of stop consonants led to a more accurate pronunciation outcome right after training (Amand & Touhami, 2016; Xi et al., 2020) and at delayed posttest (Li et al., 2021). In contrast, Hoetjes and van Maastricht (2020) used hand gestures to mimic lip rounding and tongue protrusion in order to train Dutch speakers to produce Spanish /u/ and /θ/. However, only the gestures cueing lip rounding of /u/ showed positive effects on pronunciation. Therefore, more evidence is needed to assess the role of hand gestures on the pronunciation of non-native sounds when they mimic the articulatory features of the sounds.

## 1.3 Current study

The present study examines the effectiveness of multimodal phonetic training with hand gestures encoding articulatory features (specifically, lip aperture) in producing English /æ/ and /ʌ/. Since
Catalan/Spanish speakers lack the /æ/ and /ʌ/ vowels in their native language, they tend to perceive them as a single /a/ category and produce them with smaller spectral differences compared to English speakers (Aliaga-Garcia & Mora, 2008). Formant analysis showed that Catalan/Spanish learners' productions of the /æ/ – /ʌ/ pair were closer to each other compared to native speakers. However, high-variability auditory input training had limited effects on improving their production (Aliaga-Garcia & Mora, 2008). Since better articulatory control is crucial for accurate sound production, including relevant audiovisual and gestural information in phonetic training paradigms could enhance the benefits.

To accurately pronounce the pair of English vowels /æ/ and /ʌ/, two main articulatory features are crucial, namely, tongue height and tongue backness. The first vowel is an open-mid-to-open front vowel, whereas /ʌ/ is an open-mid, central vowel (Carley & Mees, 2020). This indicates that /æ/ is pronounced with a larger lip aperture and a more fronted tongue position than /ʌ/. Following Hudson's (2011) classroom observations, we designed a hand gesture to mirror the lip aperture of the two vowels. The thumb and fingers represent the lower and upper lips, respectively. The vertical distance between them indicates the size of the lip aperture, with a larger distance for /æ/ and a shorter distance for /ʌ/ (see Figure 1).

<caption>**Figure 1**</caption>

Hand Gestures for the English Vowels /æ/ (Left Panel) and /ʌ/ (Right Panel)

<figure><img src="image_91.png" /></figure>

Based on previous research (e.g., Hoetjes & van Maastricht, 2020; Xi et al., 2020), we hypothesise that training with hand gestures mimicking lip aperture would boost Catalan/Spanish speakers' pronunciation of English /æ/ and /ʌ/ more than training without such gestures.

# 2 Research methodology

## 2.1 Participants

In a between-subjects study with a pretest-posttest-delayed posttest design, we recruited 66 Catalan/Spanish bilinguals (54 females, $M_{Age} = 19.7$ years, $SD = 1.8$) from a public university in Catalonia to learn the English vowel pair /æ/ and /ʌ/. They reported having an intermediate English proficiency level and started learning English at an average age of 5.2 years ($SD = 2.0$). None of them reported any hearing or speech impairments. All students volunteered to participate in the experiment and signed a consent form that allowed us to process their data.
Participants were randomly assigned to either the No Gesture (NG) group (n = 33, female = 26) or the Gesture (G) group (n = 33, female = 28). Both groups completed the same tests and differed only in the type of training they received. To ensure homogeneity, a series of ANOVA tests confirmed that no significant differences were found in age, age of L2 acquisition, extracurricular hours of English courses, study abroad weeks, and visuospatial working memory between the two groups (all *p* > .05).

## 2.2 Audiovisual materials for the phonetic training

The audiovisual materials for the phonetic training were recorded in a soundproof room. A General American English male speaker acted as the instructor and model speaker. For each training group, we prepared a familiarisation and training video. In the familiarisation video, the instructor provided an explicit explanation in English regarding the differences in lip aperture and tongue position between the two vowels. Especially for the G group, the instructor explained that the hand gesture represented lip aperture, in order to help participants to understand the articulatory feature and map hand gestures to articulatory information. For the training videos, we selected 6 pairs of English CVC minimal word pairs contrasting in /æ/ and /ʌ/ (e.g., *cat-cut*) and created 12 short sentences embedding each of the words (e.g., *A cat walks by*). The instructor produced the 12 words and 12 sentences while being video recorded. Again, he performed hand gestures in the G condition when producing the target vowels.

All the video clips were uploaded to the Tobii Pro Lab software<fnref n="1" /> to generate the training project for each training group with the familiarisation video preceding the training video. The software allows researchers to easily create multimodal materials (images, videos, webpages, etc.) for carrying out eye-tracking research. The clips of words and sentences were repeated three times. The whole session lasted around 15 minutes for each group.

## 2.3 Procedure

The phonetic training was performed individually in a quiet room. The NG group watched the training video without gestural input during the training, while the G group watched the video with gestural input. Participants did not imitate the speech or hand gestures. They all took the test at three points in time: before, immediately after, and one week after the training with the same tasks. To assess the learning outcome from different angles, we included three tasks: a) a less-controlled paragraph reading task — where participants read an English paragraph<fnref n="2" /> containing 14 instances of /æ/ and /ʌ/; b) a spontaneous picture naming task — where participants named 10 simple black-white line drawings<fnref n="3" /> designed to elicit 10 words containing /æ/ and /ʌ/; and c) a well-controlled word imitation task — where participants imitated 6 minimal pairs of English CVC words contrasting only in /æ/ and /ʌ/ following the instructor's model speech. While the words in the paragraph reading and picture naming tasks never appeared in the training session, half of the words used in the word imitation task were trained. Participants were audio-recorded during the three testing sessions. After the production tasks, participants did two controlled tasks: a) a language background questionnaire; and b) a symmetry span task to measure the visuospatial

<footnote n="1">¹ https://www.tobii.com/</footnote>

<footnote n="2">² The paragraph was adapted from the textbook *Phonetic Words and Stories*, Book 5 by Kathryn J. Davis.
https://www.soundcityreading.net</footnote>

<footnote n="3">³ The drawings are from https://arasaac.org/</footnote>
working memory capacity (Blacker et al., 2017). These tasks were included as previous research shows that language experience is associated with L2 pronunciation acquisition (Derwing, 2008), and visuospatial working memory correlates with the learning outcomes through instruction with hand gestures (Aldugom et al., 2020).

## 3 Data analysis and results

The first author manually annotated 6,794 target vowels using Praat (Boersma & Weenink, 2022). Among these, 129 items containing abnormal formant frequencies and 58 items with mispronunciation (i.e., the participants produced a non-target word) were excluded from the analysis. In addition, eight participants did not do the delayed posttest, and 46 items were not recorded due to technical problems. Thus, 6607 vowels were analysed.

We measured the acoustic distance between the production of the two vowels using Pillai scores. Pillai scores strongly correlate with native listeners' perceptual judgments and are useful in capturing phonetic training gains (Mora, 2021). The Pillai score ranges from 0 to 1, which reflects a speaker's overlap between two vowel realisations (Nycz & Hall-Lew, 2014). A Pillai score closer to 0 means greater acoustic overlap, whereas a score closer to 1 means greater frequency distance. We calculated the Pillai scores of the three tasks for each group at three different testing time points, using the midpoint of the vowels' first and second formants (F1 and F2). The formant values were exported using a script with the maximum number of formants set to 5 and the formant ceiling set to 5500Hz for female speakers and 5000Hz for males. Next, to account for the gender differences in vocal tract length, we normalised the formant values following the Nearey Intrinsic method with phonTools, version 0.2-2.1 (Barreda, 2015), and then averaged the values by the number of testing items. We conducted eighteen Multivariate Analyses of Variance (MANOVA) in RStudio with the normalised mean F1 and F2 mid-point values as the dependent variable and the vowel (2 levels: /æ/ and /ʌ/) as the independent variable. The summary output of each MANOVA gave the Pillai score (see Figure 2).

Table 1 shows the mean Pillai scores across the three tasks at the pretest, posttest, and delayed posttest. For the paragraph reading task, the NG group demonstrated increased overlap immediately and one week after training compared to the pretest. However, the G group showed decreased overlap after training, even though this improvement was not sustained. In the picture naming task, both groups showed decreased vowel overlap after training. The NG group demonstrated an increase in Pillai score at the posttest, followed by a decrease to the pretest level at the delayed posttest. The G group had a larger improvement from the pretest to the posttest, and although their Pillai score decreased after one week, it remained higher than the pretest score. Finally, in the word imitation task, both training groups demonstrated similar improvements in Pillai scores from the pretest to the posttest. However, at the delayed posttest, their Pillai scores decreased to a lower level than the pretest. Overall, the results suggest that hand gestures mimicking articulatory movements have limited effects on improving vowel production in the word imitation task. However, they are effective for improving vowel production in paragraph reading and picture naming tasks.
Figure 2

Vowel F1-F2 Plots with Normalised Mean Formant Values for /æ/ and /ʌ/ across Conditions and Tests from Paragraph Reading (Upper Panel), Picture Naming (Middle Panel), and Word Imitation Tasks (Lower Panel) across Groups and Tests

<figure><img src="image_92.png" /></figure>
<table><caption>**Table 1**</caption><caption>Pillai Scores across Training Conditions and Testing Time in the Three Production Tasks</caption><thead><tr><th rowspan="2"></th><th colspan="3">No Gesture</th><th colspan="3">Gesture</th></tr><tr><th>Pretest</th><th>Posttest</th><th>Delayed posttest</th><th>Pretest</th><th>Posttest</th><th>Delayed posttest</th></tr></thead><tbody><tr><th>Paragraph reading task</th><td>0.70</td><td>0.69</td><td>0.60</td><td>0.71</td><td>0.74</td><td>0.71</td></tr><tr><th>Picture naming task</th><td>0.32</td><td>0.59</td><td>0.31</td><td>0.17</td><td>0.61</td><td>0.42</td></tr><tr><th>Word imitation task</th><td>0.66</td><td>0.70</td><td>0.53</td><td>0.68</td><td>0.71</td><td>0.52</td></tr></tbody></table>

**4 Discussion and conclusion**

The present study aimed to investigate the effectiveness of hand gestures that mimic lip aperture on improving the pronunciation of non-native English vowels /æ/ and /ʌ/. A total of 66 Catalan/Spanish learners of English participated in the study, and their pronunciation performance was evaluated through three tasks: a paragraph reading task, a picture naming task, and a word imitation task. The learners' pronunciation improvement was measured using Pillai scores, which are considered a reliable indicator of vowel overlaps in L2 speech production.

The results showed that the effects of hand gestures on vowel production varied depending on the task. In the paragraph reading and picture naming tasks, training with hand gestures reduced the vowel overlap from pretest to posttest more than training without hand gestures. Although after one week the vowel overlap of /æ/ and /ʌ/ became larger (shown by the reduced Pillai score), the overlap of the NG group became larger than the pretest level. In contrast, that of the G group was either back to pretest level in the paragraph reading task or was smaller than the pretest in the picture naming task. These findings suggest that hand gestures can help reduce L2 learners' acoustic overlap of English /æ/ and /ʌ/ in the less-controlled reading task and the spontaneous production task.

However, in the more controlled word imitation task, both training groups showed similar performance, with vowel overlaps of /æ/ and /ʌ/ decreasing immediately after training but increasing beyond the pretest level after one week. This suggests that neither training method helped L2 learners sustain the improvement in the pronunciation of the target vowels. These limited effects of hand gestures on imitating non-native sounds contradict previous studies (e.g., Li et al., 2020; Xi et al., 2020), and the discrepancy could be due to differences in learners' L2 proficiency and task difficulty. While the previous studies trained naïve learners with no experience in the target L2, our participants had intermediate L2 proficiency. Therefore, the controlled imitation task might have been too easy to detect improvements.

Our results are also consistent with a previous phonetic intervention study which identified more benefits from pronunciation intervention in the discourse-reading task than in imitation tasks by English learners with elementary to intermediate levels (Ozakin et al., 2022). In addition, speech production by L2 learners in controlled tasks, such as imitation, may not necessarily reflect their productive knowledge of non-native sounds (Llompart & Reinisch, 2019). Thus, the imitation task may not be an appropriate tool to test the outcome of phonetic training gains in L2 learners with good proficiency levels.