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Plenary speakers: Abstracts

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Plenary speakers: Abstracts

Alice Henderson

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Université Grenoble-Alpes, France

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https://lidilem.univ-grenoble-alpes.fr/alice-henderson⁠

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L2 listeners' perspective on spoken language variation: From discrimination to tolerance

Much research has explored the intelligibility of L2 speech from the speaker’s perspective (see Kang et al., 2018; Levis, 2005; Munro & Derwing 2020, 1995), but this presentation will focus on the listener’s side of the story.

I will draw on quantitative and qualitative research which shows that listeners can improve their ability to adapt to new speakers and new accents. This is the basic premise of my recent, more ethnographic work in the field, training non-academic university staff at a large French public university to cope with tremendous variation in spoken English in the workplace (Henderson & Ferchiche-Jay, 2024). The overall objective is to empower people to cope better with spoken language variation, in the hope that this will also lead to more tolerance and less accent-based discrimination (see Cooper at al., 2020; Derwing & Munro, 2006; Moyer, 2013). Such a perspective is applicable to interactions via any pluricentric language (Clyne, 1992), be it English, French, Arabic, Chinese, etc.

The broader implications of accepting variation as the norm for language instruction will be examined, specifically via concrete examples of my training materials for English, used with staff working in the university’s libraries, office for international staff & students, and campus medical centre. The potential of HVPT (high variability phonetic training) to help learners (see Thomson, 2011, 2018) will also be broached, as a complementary tool.

References:

Clyne, M. G. (1992). Pluricentric languages : Differing norms in different nations, (Ed.). Mouton de Gruyter.

Cooper, B., Payne, G., Hu, X., Dixon, Q., & Kuo, L. (2020). The impact of linguistic diversity education on L1 English speakers’ ideologies, attitudes, and perceptions of international teaching assistants. In O. Kang, S. Staples, K. Yaw, & K. Hirschi (Eds.), Proceedings of the 11th Pronunciation in Second Language Learning and Teaching conference, ISSN 2380-9566, Northern Arizona University, September 2019 (pp. 49–66). Ames, IA: Iowa State University.

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

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Henderson, A., & Ferchiche, N. (2024). Listener training for library staff: Receptive accommodation at work. Speak Out! (IATEFL Pronunciation Special Interest Group Journal), 70, 29–39.

Kang, O., Thomson, R. I., & Moran, M. (2018). Empirical approaches to measuring the intelligibility of different varieties of English in predicting listener comprehension : Measuring intelligibility in varieties of English. Language Learning, 68(1), 115‑146.

https://doi.org/10.1111/lang.12270⁠

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Levis, J. M. (2005). Changing contexts and shifting paradigms in pronunciation teaching. TESOL Quarterly, 39(3), 369–377.

Moyer, A. (2013). Foreign accent : The phenomenon of non-native speech. Cambridge University Press.

Munro, M. J., & Derwing, T. M. (2020). Foreign accent, comprehensibility and intelligibility, redux. Journal of Second Language Pronunciation, 1–27.

https://doi.org/10.1075/jslp.20038.mun⁠

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Munro, M. J., & Derwing, T. M. (1995). Foreign accent, comprehensibility, and intelligibility in the speech of second language learners. Language Learning, 45(1), 73–97.

Thomson, R. I. (2018). High Variability [Pronunciation] Training (HVPT) : A proven technique about which every language teacher and learner ought to know. Journal of Second Language Pronunciation, 4(2), 208–231.

https://doi.org/10.1075/jslp.17038.tho⁠

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Thomson, R. I. (2011). Computer assisted pronunciation training : Targeting second language vowel perception improves pronunciation. CALICO Journal, 28(3), 744–765.

https://doi.org/10.11139/cj.28.3.744-765⁠

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John Levis

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Iowa State University, USA & Middle East Technical University, Türkiye

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https://engl.iastate.edu/profiles/john-levis/⁠

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Suprasegmentals, Voice Cloning, and Intelligibility in L2 Pronunciation Learning: The Present Promise of Golden Speakers

Learning the pronunciation of a new language is essential for speech intelligibility and comprehensibility (Jenkins, 2000; Levis, 2005; Munro & Derwing, 1995), which helps to ensure being understood by others during communication. The pronunciation of suprasegmentals (stress, rhythm, intonation) is especially important because suprasegmentals are difficult for many L2 users to understand and for L2 teachers to teach. In addition, multiple studies have demonstrated that improved suprasegmentals result in improved comprehensibility in spontaneous speech, while improvements in segmentals do not.

Learning to pronounce a new language requires knowing how its sound system differs from the languages you already know, what features are important to focus on for improved comprehensibility and intelligibility, and using model voices to make pronunciation features as transparent as possible. Such models, resulting from a computer resynthesis of the learner’s own voice with native pronunciation, have been called “Golden Speakers” (Ding et al., 2019; Probst, Ke & Eskenazi, 2002), and they can result in a model pronunciation that the learner can recognize as “a better me” (Henderson & Skarnitzl, 2022) that can pronounce any target language utterance with L1 suprasegmentals.

Even though the use of Golden Speaker models is promising for pronunciation learning, the use of individual golden speakers has been limited by computational power and model voices that are not wholly satisfactory. With the advent of large language models and artificial intelligence more generally, voice cloning technology has become easily available and has made it possible to create individual golden speakers with native-like suprasegmentals for any voice, combining the learner’s voice features with native prosodic patterns.

This presentation will review the importance of suprasegmentals in pronunciation learning, demonstrate how AI voice cloning technology can be used to use a learner’s own L2 voice as a pronunciation model, and provide a view of the future in which pronunciation teachers and learners benefit from AI-developed voices both in the classroom and in independent learning contexts.

References:

Ding, S., Liberatore, C., Sonsaat, S., Lučić, I., Silpachai, A., Zhao, G., Levis, J., Chukharev-Hudilainen, E., & Gutierrez-Osuna, R. (2019). Golden speaker builder – An interactive tool for pronunciation training. Speech Communication, 115, 51–66.

Henderson, A. J., & Skarnitzl, R. (2022). “A better me”: Using acoustically modified learner voices as models. Language Learning & Technology, 26(1), 1–21.

Jenkins, J. (2000). The phonology of English as an international language. Oxford University Press.

Levis, J. M. (2005). Changing contexts and shifting paradigms in pronunciation teaching. TESOL Quarterly, 39(3), 369–377.

Munro, M. J., & Derwing, T. M. (1995). Foreign accent, comprehensibility, and intelligibility in the speech of second language learners. Language Learning, 45(1), 73–97.

Probst, K., Ke, Y., & Eskenazi, M. (2002). Enhancing foreign language tutors–in search of the golden speaker. Speech Communication, 37(3-4), 161–173.

Noah Bubenhofer

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University of Zurich, Switzerland

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https://www.liri.uzh.ch/en/aboutus/Noah-Bubenhofer.html⁠

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What Linguistic Patterns Tell Us About Society

Modern semantics and pragmatics assume that language use is a mirror of linguistic action. We can always learn something about positions, opinions, and ideas in a society from the typical ways in which words or sentences are used. Corpus linguistics takes advantage of this by using large text corpora to identify and interpret these typical usages.

In my talk, I will show how even simple corpus queries, e.g., collocation analyses, can answer such questions. I also want to show that the old idea of collocations is the basis for modern machine learning methods that enable today's text-generating AI. So there are important linguistic concepts and theories behind AI. In order to understand the possibilities, but also the limitations of AI, it is therefore important to know these linguistic backgrounds, as I will show in my presentation.

Dimiter Kenarov

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American University in Bulgaria, writer, freelance journalist

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https://dimiterkenarov.com/⁠

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“You Talk like a Book, Dad”: Jeremy Irons Reading Lolita

Audio books have recently seen a significant rise in popularity. This is, of course, part of our greater cultural shift from print to audio-visual media. It’s a process that mirrors, in reverse, the move from oral performances to writing in the Early Modern period. The latter has been well documented, but what happens to works of literature specifically tailored for the page – books that rely on being books – when they are transposed onto audio? What happens to novels that are self-referential and structurally rely on the figures of the writer and the reader, when the writer turns into a reader and the reader into a listener?

This talk will focus on one of the most influential and controversial novels of the 20th century, Vladimir Nabokov’s Lolita. It will specifically consider its famous audio book version, read by the English actor Jeremy Irons, following his performance as Humbert in Adrian Lyne’s 1997 film adaptation. How does listening to Lolita, rather than reading it, change the experience of the audience? How does the disappearance of the page affect the hermeneutics of narration? What happens to the book when the book begins to talk?

Alice Henderson

L2 listeners' perspective on spoken language variation: From discrimination to tolerance

John Levis

Suprasegmentals, Voice Cloning, and Intelligibility in L2 Pronunciation Learning: The Present Promise of Golden Speakers

Noah Bubenhofer

What Linguistic Patterns Tell Us About Society

Dimiter Kenarov

“You Talk like a Book, Dad”: Jeremy Irons Reading Lolita

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