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REVIEW 4 major objections 5 minor 40 references

V(is)owel: An Interactive Vowel Chart to Understand What Makes Visual Pronunciation Effective in Second Language Learning

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read An interactive vowel chart that plots a learner's tongue position against a target speaker's line gives untrained learners a tangible goal and more practice than audio-only feedback.

desk verdict A genuinely novel interactive vowel chart with a think-aloud study, but the feedback accuracy errors are load-bearing and the abstract overstates the engagement result. read the letter →

arxiv 2507.06202 v1 pith:EPAYPG7V submitted 2025-07-08 cs.HC

Charlotte Kiesel , Dipayan Mukherjee , Mark Hasegawa-Johnson , Karrie Karahalios This is my paper · ORCID
classification cs.HC
keywords visualfeedbackcomputer-assistedpronunciationtrainingvowelchartformantextractionsecondlanguagelearninguserevaluationphoneticsthink-aloudstudy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that an interactive vowel chart, which plots a learner's first and second formants as a moving line on a tongue-position chart beside a target speaker's line, gives phonetically untrained second-language learners feedback they can act on, and that this visual guidance motivates more practice than audio-only feedback. In a within-subject think-aloud study with eight American-English speakers practicing Spanish, all eight participants described using the Spanish speaker's plotted line as a goal, and the group recorded significantly more words with the chart than with audio alone. The authors interpret this as evidence that the visualization turns pronunciation practice from relying on one's own ears into working toward a visible target. They also report that the chart's formant extraction is not yet precise enough for full trust, and they use the participants' interpretations to recommend explicit anatomical feedback that maps directly onto tongue movement.

What carries the argument

The carrier of the argument is V(is)owel, an interactive vowel chart that maps the first two formant frequencies onto a trapezoidal chart labeled with tongue height and front-back position. A projective (homography) transformation, calibrated on four English corner vowels, maps each speaker's idiosyncratic vowel space onto a fixed chart; an autocorrelation-based boundary detector finds vowel onsets and offsets; a formant extractor, selected by a smoothness metric, supplies the first and second formants; and the resulting time-varying line is paired with audio playback so learners can hear and see the same token. This machinery matters because it is what turns an untrained learner's pronunciation into a visible, comparable target line, and the think-aloud protocol reveals how learners use that line while adjusting their speech.

What would settle it

Run the same think-aloud comparison with a corrected extraction pipeline that keeps vowel-offset mean absolute error under 10 ms and second-formant mean absolute error under 100 Hz; if the practice advantage and the goal-oriented comments disappear, the reported effect depended on inaccurate visuals. A more direct test would pair the chart with ultrasound tongue imaging to check whether the plotted line matches the measured tongue position for the same recordings.

Watch

Extended reading notes

Core claim

On its own terms, V(is)owel is presented as an interactive vowel chart that accepts vowels in any word context, shows diphthong trajectories over time, and is used, as far as the authors know, in the first visual pronunciation-training study to capture learners' real-time interpretations as they adjust their pronunciation. The paper's central finding is that the plotted line acted as an external goal: all eight participants referenced the Spanish speaker's line when deciding whether to re-record, whereas with audio-only they relied on their own judgment, and participants recorded more per word with V(is)owel than with audio-only (averages of 2.27 versus 1.64 recordings per word, with a Friedman test p=0.0339). The authors conclude that the visualization provides actionable feedback because it maps tongue movement onto a physical chart, and they recommend that future pronunciation tools include explicit anatomical feedback tied to physical movement, while acknowledging that algorithmic inaccuracies, especially in the second formant and in vowel offset times, sometimes showed users incorrect lines.

Load-bearing premise

The study assumes the plotted vowel lines are accurate enough that learners' interpretations and extra practice were responses to their actual tongue position rather than to extraction artifacts; the paper's own measurements of second-formant and vowel-offset errors leave room for that assumption to fail.

Editorial extensions

If this is right

  • Designers of pronunciation feedback for phonetically untrained learners should include explicit anatomical visual feedback that maps directly onto physical movement, rather than relying on correctness colors or percentages.
  • A visible target-speaker line can increase practice volume even when learners distrust the accuracy of the visualization, because it gives them a concrete thing to work toward.
  • Vowel-focused visualization did not show a clear blinder effect in this study: participants continued to mention consonants, pitch, and overall pronunciation while practicing with V(is)owel.
  • Extracting vowels in any word context, including rhotics and diphthongs, is feasible enough for a real-time practice tool, though second-formant and vowel-offset accuracy need improvement before the feedback can be fully trusted.
  • Learners tended to trust the visualization over their own hearing, which means the accuracy of the plotted line is not just a technical detail but a central design constraint.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A corrected extraction pipeline, with vowel-offset mean absolute error under 10 ms and second-formant mean absolute error under 100 Hz, would allow a cleaner test of whether the visual-goal effect is caused by accurate articulation feedback or by the mere presence of a target line.
  • The same goal-tracking mechanism could plausibly generalize to other articulatory dimensions, such as lip rounding or nasalization, by adding equivalent visual dimensions to a chart; the paper gestures toward this in its discussion of extending V(is)owel to other languages.
  • Because learners trusted the visualization over their own ears, small algorithmic errors may be more harmful in a visual tool than in audio-only training, so accuracy requirements should be treated as a design constraint rather than a backend afterthought.
  • A longitudinal study would be needed to separate the motivating effect of novelty from the motivating effect of the visual goal itself, a limitation the authors themselves note.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces V(is)owel, an interactive vowel chart that plots a learner's first and second formants over time and plays back the associated audio, with the goal of providing actionable pronunciation feedback to phonetically untrained second-language learners. The authors report a within-subject study with eight American English speakers practicing Spanish minimal pairs under two conditions, V(is)owel and an audio-only control, using think-aloud protocols, practice counts, NASA-TLX, SUS, and exit interviews. They also report a technical evaluation of their vowel-boundary and formant-extraction algorithms against manual Praat annotations. The main claimed results are that participants practiced more with V(is)owel, that all participants used the visualized target as a goal, and that the tool provided actionable feedback that audio alone did not.

Significance. If the central claims were fully supported, the paper would make a useful contribution to computer-assisted pronunciation training by showing which aspects of articulatory visualizations help untrained learners and by demonstrating a system that works beyond isolated hVd contexts. The study has notable strengths: a counterbalanced within-subject design, think-aloud data that capture real-time interpretation, an iterative design process with pretesting, and a technical evaluation that openly benchmarks extraction accuracy against an external manual annotation standard. The paper also reports accuracy metrics transparently, which is commendable. However, the significance is undercut by a factual inconsistency in the engagement data and by accuracy levels that are acknowledged as faulty in the Discussion; both issues bear directly on the claim that V(is)owel provides actionable feedback. The absence of any pronunciation improvement measure further limits the scope of the conclusions.

major comments (4)
  1. [Abstract, §5.1.3, Table 3] The abstract states that "all participants practicing words with V(is)owel more than with audio-only," but Table 3 shows that P7 recorded fewer times per word with V(is)owel (1.875) than with audio-only (2.250). This contradiction invalidates the "all participants" claim and should be corrected in the abstract and in Section 5.1.3. The Friedman test result may still hold, but the summary of the finding must not claim unanimity where the data show an exception.
  2. [§5.3.1, §5.3.2, §5.4, §5.5] The technical evaluation reports F2 mean absolute error of 116.3 Hz against a 100 Hz tolerance, vowel-offset MAEs up to 265 ms (Table 4), and large formant-error rates of 5–26% per participant (Table 6). Section 5.4 attributes the higher practice counts to participants having "guidance on what to change," and Section 5.5 states that participants "put greater emphasis on the visualization than on their perception of pronunciation." If a substantial fraction of the plotted lines are misleading, then the behavioral and think-aloud evidence is partly a reaction to incorrect visuals, and the claim that V(is)owel provides "actionable feedback" is not supported in its current form. The paper needs either an accuracy-conditioned analysis (e.g., comparing engagement and interpretations on trials with small versus large formant errors) or a substantially weakened claim that separates perceived guidance from verified guidance.
  3. [§7, Abstract] The study measures engagement, self-reported confidence, and think-aloud interpretations, but it does not measure pronunciation improvement before and after practice. Nevertheless, the conclusion in Section 7 states that "personalized feedback can have a significant impact on a learners' pronunciation in a second language," and the abstract opens with a general claim that "visual feedback speeds up learners' improvement." These causal learning claims are not supported by the reported data. The paper should either add a learning-outcome measure or explicitly reframe the conclusions as being about engagement and perceived actionability rather than demonstrated pronunciation gains.
  4. [Discussion] The Discussion states that "based on offset times, V(is)owel currently gives faulty feedback," which directly conflicts with the abstract's claim that "V(is)owel is effective at providing actionable feedback." This tension is not merely stylistic: faulty feedback can be actionable in the narrow sense that users can act on it, but the paper does not establish that acting on it moves learners toward the target. The authors should reconcile these statements, either by defining the limited sense in which feedback is actionable or by removing the effectiveness claim.
minor comments (5)
  1. [§3.2.2] The sentence "We use Praat's format extraction library" should read "formant extraction library."
  2. [§5.1.2, Familiarity] The text says "Pt expressed how this made it more difficult to reach their goals," where "Pt" appears to be a placeholder for a participant identifier; please replace it with the intended label.
  3. [§5.1.2, Using the Spanish Speaker's Line as a Goal] The phrase "far away from the spa line" appears to be a typo; it should presumably be "Spanish speaker's line."
  4. [Table 5 caption] The caption says "Bold indicates an offset that is outside allowed tolerance," but the table contains formant frequency MAE values and no bold entries; the caption appears to have been copied from Table 4 and should be corrected.
  5. [Appendix A.1] The Friedman test result would be more informative if the test statistic and effect size were reported alongside the p-value, given the small sample size of eight participants.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the study's claims are empirical, measured against external benchmarks, and no prediction reduces to a fitted input or self-citation.

full rationale

The paper contains no derivation chain that reduces to its own inputs. Its central claims are empirical observations from an eight-participant within-subject study: participants recorded more words with V(is)owel than with audio-only (Friedman p=0.0339), and think-aloud comments indicated that the visual target served as a goal. These are measurements, not values predicted from a fitted model. The only fitted component is the speaker-calibration homography, which maps four corner vowels to a fixed trapezoid; it is a speaker normalization, and the plotted positions of practice vowels are measured formant values transformed by that map, not predictions of the outcome claim. The technical evaluation is benchmarked externally against manual Praat annotations (Tables 4-6), and the paper explicitly reports large errors, including F2 MAE above tolerance and offsets up to 265 ms, rather than hiding them. The discussion even concedes that V(is)owel 'currently gives faulty feedback.' The conclusion that feedback is 'actionable' is inferred from participant statements and practice counts; whether that inference is valid despite the accuracy faults is a correctness and construct-validity concern, not a circularity concern. There are no load-bearing self-citations, no imported uniqueness theorems, and no renamed known result. Accordingly, no circular step can be exhibited, and the appropriate score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claims rest on standard phonetic mapping assumptions, qualitative self-report, and the accuracy of the formant extraction. The most fragile input is the hand-tuned and unreported autocorrelation threshold, plus the assumption that displayed F1 and F2 lines faithfully represent tongue position; both influence every participant-visible plot.

free parameters (3)
  • Autocorrelation threshold for vowel onset and offset = not reported (tuned by trial and error)
    Section 3.2.1: the threshold was 'set by experimenting on different words and speakers'; this parameter directly determines which audio segment is extracted and displayed as the vowel.
  • Vowel chart trapezoid ratio = height 1, top width 1.33, bottom width 0.833
    Section 3.1.1: chosen by the research team because 'there is no set ratio for vowel chart trapezoids'; it shapes every plotted position on the chart.
  • Formant-count smoothness metric coefficient = 0.8^(i-2)
    Section 3.2.2: the metric that selects the number of formants uses a hand-chosen exponential weight; it determines which formant track is labeled F1 and F2.
assumptions (4)
  • domain assumption Formant frequencies F1 and F2 map monotonically to tongue height and tongue frontness or backness.
    The entire vowel chart design in Section 3.1 assumes this standard phonetic mapping, but the study never measures tongue position physiologically for each participant.
  • domain assumption Manual Praat formant annotations are an acceptable ground truth for evaluating extraction accuracy.
    The technical evaluation in Section 5.3 compares the algorithm's outputs to manually extracted formants by Praat; the tool's own accuracy is judged against this external standard.
  • domain assumption Think-aloud utterances and exit interviews reflect participants' underlying cognitive processes.
    All research-question conclusions rely on coded think-aloud and interview data as described in Section 4.4; this is the standard qualitative assumption but is not independently validated.
  • ad hoc to paper The autocorrelation threshold identifies vowel boundaries correctly across speakers and words.
    Section 3.2.1 describes the threshold as set by trial and error rather than derived from a theoretical criterion; it is a paper-specific tuning choice that drives every extracted vowel.

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Cite this review

Pith. "Pith review of V(is)owel: An Interactive Vowel Chart to Understand What Makes Visual Pronunciation Effective in Second Language Learning." pith.science (2026). https://pith.science/paper/EPAYPG7V

@misc{pith2026250706202,
  author       = {Pith},
  title        = {Pith review of: V(is)owel: An Interactive Vowel Chart to Understand What Makes Visual Pronunciation Effective in Second Language Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EPAYPG7V}},
  note         = {Machine review of arXiv:2507.06202}
}
read the original abstract

Visual feedback speeds up learners' improvement of pronunciation in a second language. The visual combined with audio allows speakers to see sounds and differences in pronunciation that they are unable to hear. Prior studies have tested different visual methods for improving pronunciation, however, we do not have conclusive understanding of what aspects of the visualizations contributed to improvements. Based on previous work, we created V(is)owel, an interactive vowel chart. Vowel charts provide actionable feedback by directly mapping physical tongue movement onto a chart. We compared V(is)owel with an auditory-only method to explore how learners parse visual and auditory feedback to understand how and why visual feedback is effective for pronunciation improvement. The findings suggest that designers should include explicit anatomical feedback that directly maps onto physical movement for phonetically untrained learners. Furthermore, visual feedback has the potential to motivate more practice since all eight of the participants cited using the visuals as a goal with V(is)owel versus relying on their own judgment with audio alone. Their statements are backed up by all participants practicing words with V(is)owel more than with audio-only. Our results indicate that V(is)owel is effective at providing actionable feedback, demonstrating the potential of visual feedback methods in second language learning.

Figures

Figures reproduced from arXiv: 2507.06202 by the authors.

Figure 1
Figure 1. Four types of pronunciation visualizations. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The study begins by calibrating a user’s voice using extracted formants of four English vowels [ [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Example practice pages from audio-only and [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (11 more)
Figure 5
Figure 5. Figure 5: A mock-up of two vowels from one word plotted [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: V(is)owel with Spanish vowels marked by A and B and user’s vowels labeled numerically with the latest record￾ing starting at 1. while physical labels (front, back, high, low) were more helpful in interpretation. Although V(is)owel has bounding grid lines, vowels are no…
Figure 7
Figure 7. Figure 7: On the left is an example frequency shaped trape [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Design cycle of page 2 3.2.1 Vowel Extraction We designed a vowel extraction algorithm based on the autocorrelation of the signal after testing two existing algorithms that did not have the precision needed, Dynamic Time Warping (DTW) [2] and PocketSphinx Phoneme Align…
Figure 9
Figure 9. Figure 9: A flowchart representing the algorithmic process of extracting vowels and formants. [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: An example graph we made demonstrating the [PITH_FULL_IMAGE:figures/full_fig_p007_10.png]
Figure 12
Figure 12. Figure 12: A flowchart representing the progress of the study. The orange boxes represent the fixed portions of the study, the [PITH_FULL_IMAGE:figures/full_fig_p009_12.png]
Figure 13
Figure 13. Figure 13: Average scores from surveys. 5.1.5 System Usability All but P2 rated both practice systems with an above 65 for system usability (Figure 13b). Again, audio-only seems to be easier to use, likely because even if the algorithm did not detect a vowel, an playback box wou…
Figure 14
Figure 14. Figure 14: Two colorblind friendly palettes. From left to right: [PITH_FULL_IMAGE:figures/full_fig_p017_14.png]
Figure 15
Figure 15. Figure 15: Three potential layouts for V(is)owel practice. The vowel chart is outlined in black and the recording buttons and recordings are outlined in grey. Audio-only practice is just the grey portion. (a) The paper prototype of the first page of the tutorial. A grid of four …
Figure 17
Figure 17. Figure 17: Design cycle of page 1 [PITH_FULL_IMAGE:figures/full_fig_p018_17.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

40 extracted references · 37 canonical work pages

  1. [1]

    Jessica Barlow and Judith Gierut. 2002. Minimal Pair Approaches to Phonological Remediation. Seminars in speech and language 23 (03 2002), 57–68. https: //doi.org/10.1055/s-2002-24969

    work page doi:10.1055/s-2002-24969 2002

  2. [2]

    Bellman and R

    R. Bellman and R. Kalaba. 1959. On adaptive control processes. IRE Transactions on Automatic Control 4, 2 (1959), 1–9. https://doi.org/10.1109/TAC.1959.1104847

    work page arXiv 1959

  3. [3]

    Cindy Blanco and Ari Moline. 2025. Covering all the bases: Duolingo’s approach to listening skills. https://blog.duolingo.com/covering-all-the-bases-duolingos- approach-to-listening-skills/

    work page 2025

  4. [4]

    Heather Bliss, Jennifer Abel, and Bryan Gick. 2018. Computer-Assisted Visual Articulation Feedback in L2 Pronunciation Instruction: A Review. Journal of Second Language Pronunciation 4 (05 2018), 129–153. https://doi.org/10.1075/ jslp.00006.bli

    work page 2018

  5. [5]

    1992-2022

    Paul Boersma and David Weenink. 1992-2022. Praat: doing phonetics by computer. https://www.fon.hum.uva.nl/praat/ [Computer program]

    work page 1992

  6. [6]

    Paul A. Booth. 1991. Errors and theory in human-computer interaction. Acta Psychologica 78, 1 (1991), 69–96. https://doi.org/10.1016/0001-6918(91)90005-K

    work page doi:10.1016/0001-6918(91)90005-k 1991

  7. [7]

    Adam Brown. 1995. Minimal pairs: minimal importance? ELT Journal 49, 2 (04 1995), 169–175. https://doi.org/10.1093/elt/49.2.169 arXiv:https://academic.oup.com/eltj/article-pdf/49/2/169/9791152/169.pdf

    work page doi:10.1093/elt/49.2.169 1995

  8. [8]

    Yaohua Bu, Tianyi Ma, Weijun Li, Hang Zhou, Jia Jia, Shengqi Chen, Kaiyuan Xu, Dachuan Shi, Haozhe Wu, Zhihan Yang, et al. 2021. PTeacher: a Computer-Aided Personalized Pronunciation Training System with Exaggerated Audio-Visual Corrective Feedback. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–14

    work page 2021

Show all 40 references
  1. [9]

    Michael Carey. 2004. CALL Visual Feedback for Pronunciation of Vowels: Kay Sona-Match. CALICO Journal 21, 3 (2004), 571–601. https://www.jstor.org/ stable/24149798 Publisher: Equinox Publishing Ltd

    2004

  2. [10]

    Qiaoyi Chen, Siyu Liu, Kaihui Huang, Xingbo Wang, Xiaojuan Ma, Junkai Zhu, and Zhenhui Peng. 2024. RetAssist: Facilitating Vocabulary Learners with Generative Images in Story Retelling Practices. In Proceedings of the 2024 ACM Designing Interactive Systems Conference (Copenhag...

    2024

  3. [11]

    Isabelle Darcy, Brian Rocca, and Zoie Hancock. 2021. A Window into the Class- room: How Teachers Integrate Pronunciation Instruction. RELC Journal 52, 1 (2021), 110–127. https://doi.org/doi/10.1177/0033688220964269

    2021 doi

  4. [12]

    Keya Rani Das and AHMR Imon. 2016. A brief review of tests for normality. American Journal of Theoretical and Applied Statistics 5, 1 (2016), 5–12

    2016

  5. [13]

    Tracey Derwing. 2003. What Do ESL Students say about Their Accents?Canadian Modern Language Review-revue Canadienne Des Langues Vivantes - CAN MOD LANG REV 59 (06 2003), 547–567. https://doi.org/10.3138/cmlr.59.4.547

    2003 doi

  6. [14]

    Greene, David B

    Beth G. Greene, David B. Pisoni, and Thomas D. Carrell. 1984. Recognition of speech spectrograms. The Journal of the Acoustical Society of America 76, 1 (July 1984), 32–43. https://doi.org/10.1121/1.391035

    1984 doi

  7. [15]

    Mohd Hilmi Hamzah and Abdullah Bawodood. 2019. TEACHING ENGLISH SOUNDS VIA MINIMAL PAIRS: THE CASE OF YEMENI EFL LEARNERS.Journal of English Language and Literature 6 (10 2019), 97–102. https://doi.org/10.33329/ joell.63.97

    2019

  8. [16]

    Feifei Han. 2016. Integrating pronunciation instruction with passage-level read- ing instruction. Pronunciation in the classroom: The overlooked essential (2016), 143–151

    2016

  9. [17]

    Sandra G Hart. 1986. NASA task load index (TLX). (1986)

    1986

  10. [18]

    Soon-Hin Hew and Mitsuru Ohki. 2004. Effect of Animated Graphic Annotations and Immediate Visual Feedback in Aiding Japanese Pronunciation Learning: A Comparative Study. CALICO Journal 21, 2 (2004), 397–419. https://www.jstor. org/stable/24149403 Publisher: Equinox Publishing Ltd

    2004

  11. [19]

    Martin Hinton. 2013. An Aptitude for Speech: The Importance of Mimicry Ability in Foreign Language Pronunciation. In Teaching and Researching English Accents in Native and Non-native Speakers , Ewa Waniek-Klimczak and Linda R. Shockey (Eds.). Springer, 103–111. https://doi.org...

    2013 doi

  12. [20]

    Murat Hismanoglu and Sibel Hismanoglu. 2010. Language teachers’ preferences of pronunciation teaching techniques: traditional or modern? Procedia-Social and Behavioral Sciences 2, 2 (2010), 983–989

    2010

  13. [21]

    David Huggins-Daines, Mohit Kumar, Arthur Chan, Alan W Black, Mosur Ravis- hankar, and Alexander I Rudnicky. 2006. Pocketsphinx: A free, real-time contin- uous speech recognition system for hand-held devices. In 2006 IEEE international conference on acoustics speech and signal...

    2006

  14. [22]

    Yannick Jadoul, Bill Thompson, and Bart De Boer. 2018. Introducing parselmouth: A python interface to praat. Journal of Phonetics 71 (2018), 1–15

    2018

  15. [23]

    Kelly Moser and Tianlan Wei. 2024. COVID-19 and the pre-existing language teacher supply crisis. Language Teaching Research 28, 5 (2024), 1940–1975

    2024

  16. [24]

    Murray J Munro, Tracey M Derwing, and Ron I Thomson. 2015. Setting segmental priorities for English learners: Evidence from a longitudinal study. International Review of Applied Linguistics in Language Teaching 53, 1 (2015), 39–60

    2015

  17. [25]

    Offerman and Daniel J

    Heather M. Offerman and Daniel J. Olson. 2016. Visual feedback and second language segmental production: The generalizability of pronunciation gains. System 59 (July 2016), 45–60. https://doi.org/10.1016/j.system.2016.03.003

    2016 doi

  18. [26]

    Daniel J Olson. 2014. Benefits of visual feedback on segmental production in the L2 classroom. (2014)

    2014

  19. [27]

    Annu Paganus, Vesa-Petteri Mikkonen, Tomi Mäntylä, Sami Nuuttila, Jouni Isoaho, Olli Aaltonen, and Tapio Salakoski. 2006. The vowel game: continuous real-time visualization for pronunciation learning with vowel charts. InAdvances in Natural Language Processing: 5th Internation...

    2006

  20. [28]

    Indah Rahman and Isna Nur. 2017. THE USE OF MINIMAL PAIR TECHNIQUE IN TEACHING PRONUNCIATION AT THE SECOND YEAR STUDENTS OF SMAN 4 BANTIMURUNG. 4 (2017), 276. https://doi.org/10.24252/Eternal.V42.2018.A11

    2017 doi

  21. [29]

    Rajiv Rao. 2019. Key issues in the teaching of Spanish pronunciation . Routledge London, United Kingdom

    2019

  22. [30]

    Ivana Rehman. 2021. Real-time formant extraction for second language vowel production training. Ph. D. Dissertation. Iowa State University

    2021

  23. [31]

    Schafer and Lawrence R

    Ronald W. Schafer and Lawrence R. Rabiner. 1970. System for Au- tomatic Formant Analysis of Voiced Speech. The Journal of the Acoustical Society of America 47, 2B (02 1970), 634–648. https: //doi.org/10.1121/1.1911939 arXiv:https://pubs.aip.org/asa/jasa/article- pdf/47/2B/634/...

    1970 doi

  24. [32]

    Michael R Sheldon, Michael J Fillyaw, and W Douglas Thompson. 1996. The use and interpretation of the Friedman test in the analysis of ordinal-scale data in repeated measures designs. Physiotherapy Research International 1, 4 (1996), 221–228

    1996

  25. [33]

    Laura Sicola and Isabelle Darcy. 2015. Integrating pronunciation into the lan- guage classroom. The handbook of English pronunciation (2015), 471–487

    2015

  26. [34]

    Lars St, Svante Wold, et al. 1989. Analysis of variance (ANOVA). Chemometrics and intelligent laboratory systems 6, 4 (1989), 259–272

    1989

  27. [35]

    Magdalena Szyszka. 2015. Good English Pronunciation Users and Their Pro- nunciation Learning Strategies. Research in Language 13 (03 2015), 93–106. https://doi.org/10.1515/rela-2015-0017

    2015 doi

  28. [36]

    Jeff Tennant and RC Gardner. 2004. The computerized mini-AMTB. Calico Journal (2004), 245–263

    2004

  29. [37]

    R. I. Thomson and T. M. Derwing. 2014. The Effectiveness of L2 Pronunciation Instruction: A Narrative Review. 36, 3 (12 2014), 326–344. https://doi.org/10. 1093/applin/amu076

    2014

  30. [38]

    Michael Wei. 2006. A Literature Review on Strategies for Teaching Pronunciation. Online submission (2006)

    2006

  31. [39]

    Martijn Wieling, Eliza Margaretha, and John Nerbonne. 2011. Inducing phonetic distances from dialect variation. Computational Linguistics in the Netherlands Journal 1 (2011), 109–118

    2011

  32. [40]

    weak ... strong

    Ka-Wa Yuen, Wai-Kim Leung, Peng-fei Liu, Ka-Ho Wong, Xiao-jun Qian, Wai-Kit Lo, and Helen Meng. 2011. Enunciate: An internet-accessible computer-aided pronunciation training system and related user evaluations. In 2011 International Conference on Speech Database and Assessment...

    2011

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