Second Language Pronunciation. Группа авторов

and Lyster (2016) provide several examples of perception activities, many traditional communicative language tasks could be adapted with similar results, such as information gap activities or find the difference tasks, in which minimal pairs of interest can be targeted.

      The most obvious challenge to implementing these evidence-based activities in a classroom is the need for multiple instructors to provide the necessary input variability. It would be more practical to utilize recorded speech of multiple talkers, with immediate feedback still provided by the instructor. In mixed L1 classrooms, it might be possible to allow learners to provide input to each other, if they can be paired in such a way that members of each pair don’t have the same L2 pronunciation difficulties. For example, a Spanish L1 speaker could provide /l/-/ɹ/ pairs to a Japanese L1 speaker for whom this pair is problematic.

      Practical Resources for Pedagogy

      While many HVPT studies have examined this technique’s efficacy in the lab, their focus tends to be very narrow, investigating only a small number of sounds in a small number of phonetic or word contexts. As such, they do not comprise complete training systems which can be turned public-facing for consumption by a wide range of learners. Nor do they typically provide the opportunity for scalable research, since they are largely designed for one-off studies investigating a specific pronunciation issue faced by a particular group of learners. Even platforms that have been used to conduct research in the context of real language programs suffer from similar limitations (e.g., Thomson, 2011, 2012a; Wang & Munro, 2004.

A handful of HVPT researchers have attempted to make their platforms available to the public. Iverson and Evans’ (2009) British English Vowel Trainer was a mobile HVPT application, but it no longer seems to exist. Linguatorium Auris (LA) (Linguatorium Smart Language Systems, 2021), is an empirically validated HVPT system (Qian et al., 2018), bundled with a vocabulary training application. One of LA’s strengths is that it includes a variety of learning tasks beyond those normally associated with HVPT. This should encourage learner engagement. LA is also adaptive, targeting only those sounds that the system determines a particular user needs to target. A limitation of LA is that it only utilizes two training talkers, both of which are artificial. Thus, despite constituting HVPT in terms of its use of variable phonetic contexts, it only marginally qualifies as HVPT with respect to its number of talkers. English Accent Coach (EAC) (Thomson, 2018b) is largely a traditional listen-and-click version of HVPT training for vowels and consonants. It also includes a game called Echo (reminiscent of the 1980s Simon game, but with speech sounds instead of musical tones). EAC utilizes 30 talkers, producing North American English sounds in nearly every phonetic context. It also allows selection of sounds in either nonsense syllables or in real words. Unlike LA, EAC is not adaptive, requiring that users determine their own focus of instruction.

      Both LA and EAC allow learners to create personal accounts to track their progress over time. LA is offered on a per semester basis, for a nominal fee, whereas EAC is currently free. Thomson (2012b) provides a detailed teacher-oriented description of EAC, along with some suggestions for activities to extend learning from the computer to the classroom. LA has less public documentation. Ultimately, both applications would benefit from built-in modules to encourage learners to practice the production of sounds that they are learning to perceive. This may facilitate faster transfer to production.

      While it may be theoretically possible for instructors to build their own HVPT systems, if the goal is to include many talkers and sounds in many contexts, the recording process alone is a massive obstacle to overcome. Ideally, publicly available programs would provide the basic infrastructure and sound database, with the possibility of instructors adding targeted recordings of particular words as needed. With improvements in Text-to-Speech technology, it might be possible to create stimuli using a wider range of artificial voices, assuming that they all reflect the same target accent variety.

      Conclusion

      In this chapter, I have argued that just as speech perception plays a central role in L1 pronunciation development, it is also essential to L2 pronunciation learning. Despite this fact, most instruction continues to be production-oriented. The mismatch between what researchers know about L2 pronunciation development and what teachers practice is in part attributable to a lack of communication between researchers and teachers. Most of the L2 speech perception literature is not written for an applied audience. I have attempted to bridge that gap by highlighting key information to allow teachers to incorporate evidence-based principles from the literature into their pronunciation teaching. The chapter also focuses on some promising developments in public-facing perceptual training applications and platforms, which will hopefully continue to move this area of research forward, with resulting benefits for learners.

      Postscript

At the time of writing, my son Elliot is now five. For over a year, he has been very concerned about what he calls the “Canonivirus.” Because we think it is cute, nobody in my family draws his attention to the mismatch between his pronunciation and our own frequent references to the “Coronavirus,” which he clearly understands to be the same thing. This provides another example of how attempting to imitate speech, before being able to accurately perceive it, may result in mispronunciation that will require conscious attention to correct. At some point in the not-too-distant-future, Elliot’s cognitive speech learning mechanisms will surely help him close this gap and he will correct his mispronunciation. L2 learners can accomplish the same thing with the right type of perception-oriented training.

      Author Biography

      Ron Thomson is Professor of Applied Linguistics/TESL at Brock University. His research interests include the relationship between L2 speech perception and production, L2 oral fluency, and the use of technology to promote L2 pronunciation learning. He is also the creator of www.englishaccentcoach.com, an online perception-oriented pronunciation trainer.

      References

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