---
title: "Pronunciation recognition of English phonemes /\\textipa{@}/, /æ/,"
description: "The paper asks how well two classic acoustic feature sets can tell apart four English vowels that learners often… 70% accuracy. 27 references."
authors: ["Keith Y. Patarroyo", "Vladimir Vargas-Calderon"]
year: 2017
venue: "arXiv:1702.07071"
source: "https://arxiv.org/abs/1702.07071"
doi: "10.48550/arxiv.1702.07071"
arxiv: "1702.07071"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/patarroyo-2017-pronunciation-recognition-english-phonemes-textipa/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/patarroyo-2017-pronunciation-recognition-english-phonemes-textipa/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2017](https://md.research.elamur.ai/free/en/papers/2017/page/5/) / Pronunciation recognition of English phonemes /\\textipa{@}/, /æ/, /\\textipa{A}:/ and /\\textipa{2}/ using Formants and Mel Frequency Cepstral Coefficients

# Pronunciation recognition of English phonemes /\\textipa{@}/, /æ/, /\\textipa{A}:/ and /\\textipa{2}/ using Formants and Mel Frequency Cepstral Coefficients

**Authors:** Keith Y. Patarroyo and Vladimir Vargas-Calderon · **Year:** 2017 · **Venue:** arXiv:1702.07071 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1702.07071) · [PDF](https://arxiv.org/pdf/1702.07071) · [DOI 10.48550/arxiv.1702.07071](https://doi.org/10.48550/arxiv.1702.07071) · [arXiv 1702.07071](https://arxiv.org/abs/1702.07071) · [OpenAlex](https://openalex.org/W2593743398)

**Citations:** 1 citations (OpenAlex, 2026-10-14)

## Summary
The paper asks how well two classic acoustic feature sets can tell apart four English vowels that learners often confuse: /@/, /æ/, /A:/ and /2/. Spanish and Italian speakers, for example, tend to merge them into one /a/. The motivation is automatic pronunciation assessment for language teaching. The authors took recordings by native speakers from the Vocal Joystick Vowel Corpus. They used short and long, single-level-intonation files at quiet, normal or loud amplitude. For each file they removed silence, normalised amplitude, cut a 40 ms segment and applied a Hamming window. They then extracted the first two formants via Linear Predictive Coding and 13 MFCCs, and trained a Decision Tree Classifier on each feature set. Formants reached about 70% accuracy. MFCCs performed worse on the four confusable vowels (52–56%) but clearly better on distinct vowels (up to 88%). The authors conclude that these standard tools miss the fine distinctions human listeners make. That limits their use as objective pronunciation judges.

## Key points
- Data come from the Vocal Joystick Vowel Corpus (Bilmes et al., 2006). The file count is reported inconsistently: 748 in the abstract, 784 in the methods. After removing formant outliers beyond 1.5σ of each vowel's mean, 642 signals remained.
- Features were F1 and F2 from LPC, and 13 MFCCs. The MFCCs were projected to 2D with PCA only for visualisation. The classifier in every case was a Decision Tree Classifier.
- With the two formants, all four vowels were classified at about 70% accuracy. The measured formant means were compared against Hawkins et al., Hunter et al. and Deterding, showing large variation across studies.
- With MFCCs, the four confusable vowels gave 52% (abstract) or 56% (body text). The body reports 71% after dropping /A:/, which overlaps with /2/, while the abstract says /@/ was dropped.
- As a control with dissimilar vowels (/2/, /e/, /u/, /o/), MFCCs reached 73%, and 88% once /o/ (mixed with /u/) was removed.

## Interesting ideas
- The authors check a feature set on a deliberately easy contrast set as well as the hard one, so a weak result can be blamed on the task rather than the pipeline.
- They project the features onto a plane to see which classes overlap, then remove the overlapping class and re-measure to confirm the confusion.
- They drop per-class outliers beyond 1.5 standard deviations before training, which removes mis-measured or mislabelled samples cheaply.
- They compare their own measured values against several published reference tables as a sanity check, and treat disagreement between sources as a sign of real population variation.
- They pair a few interpretable features (two formants) with a transparent classifier, giving a baseline whose decisions can be read directly.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (learn): Evaluation could pair each hard benchmark slice with an "easy" control slice, so that a drop in parser or reader accuracy can be read as a real difficulty effect and not a broken component.
- **I2** (tokens): Tagger errors could be diagnosed the same way: find which POS or morphology classes collapse into each other, then retest without one of them to localise the ambiguity.
- **I3** (learn): Silver data agreed by witness parsers could get a similar per-label statistical filter, for example on arc length or attachment-score distributions, to drop suspicious examples before training.
- **I4** (world): Compiled measures and values in the knowledge layer could be cross-checked against multiple sources, recording the spread instead of one number when sources disagree.
- **I5** (tree): A small, inspectable feature-plus-decision-rule baseline for attachment choices would give an explainable reference point against which gparse's gains can be measured and argued.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

📚 Shelves (7): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works · [💭 Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) · 798 works · [💭 Experimental and Cognitive Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · 463 works · [🔊 Signal Processing](https://md.research.elamur.ai/free/en/shelves/computer-science/signal-processing/) · 213 works

🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 1,373 works

## Cite

Cite as: Keith Y. Patarroyo and Vladimir Vargas-Calderon. 2017. Pronunciation recognition of English phonemes /\\textipa{@}/, /æ/, /\\textipa{A}:/ and /\\textipa{2}/ using Formants and Mel Frequency Cepstral Coefficients. arXiv:1702.07071.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{patarroyo2017pronunciation,
  title = {{Pronunciation recognition of English phonemes /textipa@/, /æ/, /textipaA:/ and /textipa2/ using Formants and Mel Frequency Cepstral Coefficients}},
  author = {Keith Y. Patarroyo and Vladimir Vargas-Calderon},
  year = {2017},
  doi = {10.48550/arxiv.1702.07071},
  eprint = {1702.07071},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1702.07071}
}
```

- [← previous Prepositional Phrase Attachment over Word Embedding Products](https://md.research.elamur.ai/free/en/papers/madhyastha-2017-prepositional-phrase-attachment-over-word/)
- [next → RESEÑA: Peck, John y Martin Coyle, 2002 (211-215), A Brief History of English Literature, 353 pp](https://md.research.elamur.ai/free/en/papers/sanchez-2017-resena-peck-john-y-martin/)
- [↑ back to papers of 2017](https://md.research.elamur.ai/free/en/papers/2017/page/5/)

## References

27 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Baker, J., Li Deng, Glass, J., Khudanpur, S., Chin-hui Lee, Morgan, N., & O’Shaughnessy, D. (2009). Developments and directions in speech recognition and understanding, Part 1 [DSP Education]. IEEE Signal Processing Magazine, 26(3), 75-80. http://dx.doi.org/10.1109/msp.2009.932166 [↗](https://doi.org/10.1109/msp.2009.932166)
2. Bellini, D. (n.d.). Expressive Digital Signal Processing (DSP) package for Python, 2016. Retrieved November 16, 2016, from [https://github.com/danilobellini/](https://github.com/danilobellini/) audiolazy [↗](https://github.com/danilobellini/)
3. Bilmes, J., Wright, R., Xiao, L., Malkin, J. & Kilanski, K. 2006. [http://melodi.ee.washington.edu/vj/index.html](http://melodi.ee.washington.edu/vj/index.html) [↗](http://melodi.ee.washington.edu/vj/index.html)
4. Davis, S. & Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions On Acoustics, Speech, And Signal Processing, 28(4), 357-366. http://dx.doi.org/10.1109/tassp.1980.1163420 [↗](https://doi.org/10.1109/tassp.1980.1163420)
5. Deng, L. & O’Shaughnessy, D. (2003). Speech processing (1st ed., pp. 19-26). New York: Marcel Dekker. [find](https://scholar.google.com/scholar?q=Deng%2C+L%2E+%26+O%E2%80%99Shaughnessy%2C+D%2E+%282003%29%2E+Speech+processing+%281st+ed%2E%2C+pp%2E+19%2D26%29%2E+New+York%3A+Marcel+Dekker%2E)
6. Deterding, D. (2006). The North Wind versus a Wolf: short texts for the description and measurement of English pronunciation. Journal Of The International Phonetic Association, 36(02), 187. http://dx.doi.org/10.1017/ s0025100306002544 [↗](https://doi.org/10.1017/s0025100306002544)
7. Ghaffarvand Mokari, P. & Werner, S. (2016). Perceptual assimilation predicts acquisition of foreign language sounds: The case of Azerbaijani learners’ production and perception of Standard Southern British English vowels. Lingua, 185, 81-95. http://dx.doi.org/10.1016/j.lingua.2016.07.008 [↗](https://doi.org/10.1016/j.lingua.2016.07.008)
8. Hawkins, S. & Midgley, J. (2005). Formant frequencies of RP monophthongs in four age groups of speakers. Journal Of The International Phonetic Association, 35(02), 183. http://dx.doi.org/10.1017/s0025100305002124 [↗](https://doi.org/10.1017/s0025100305002124)
9. Hillenbrand, J., Getty, L., Clark, M., & Wheeler, K. (1995). Acoustic characteristics of American English vowels. The Journal Of The Acoustical Society Of America, 97(5), 3099. http://dx.doi.org/10.1121/1.411872 [↗](https://doi.org/10.1121/1.411872)
10. Hincks, R. (2003). Speech technologies for pronunciation feedback and evaluation. Recall, 15(01). http://dx.doi.org/10.1017/s0958344003000211 [↗](https://doi.org/10.1017/s0958344003000211)
11. Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., & Jailty, N. et al. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition. Retrieved from [http://static.googleusercontent.com/media/research.google.com/](http://static.googleusercontent.com/media/research.google.com/) en//pubs/archive/38131.pdf [↗](http://static.googleusercontent.com/media/research.google.com/)
12. Hunter, G. & Kebede, H. (2012). Formant frequencies of British English vowels produced by native speakers of Farsi. Société Française d’Acoustique. Retrieved from [https://hal.archives-ouvertes.fr/hal-00810580/document](https://hal.archives-ouvertes.fr/hal-00810580/document) [↗](https://hal.archives-ouvertes.fr/hal-00810580/document)
13. Lee, K. (1988). On large-vocabulary speaker-independent continuous speech recognition. Speech Communication, 7(4), 375-379. http://dx.doi.org/10. 1016/0167-6393(88)90053-2 [↗](https://doi.org/10.1016/0167-6393(88)90053-2)
14. Lyons, J. Mel Frequency Cepstral Coefficient (MFCC) tutorial, Practical Cryptography. Retrieved October 15, 2016, from [http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/](http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/) [↗](http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/)
15. Martin, I. & Jackson, C. (2016). Pronunciation Training Facilitates the Learning and Retention of L2 Grammatical Structures. Foreign Language Annals, 49(4), 658-676. http://dx.doi.org/10.1111/flan.12224 [↗](https://doi.org/10.1111/flan.12224)
16. Mirzaei, K., Gowhary, H., Azizifar, A., & Esmaeili, Z. (2015). Comparing the Phonological Performance of Kurdish and Persian EFL Learners in Pronunciation of English Vowels. Procedia - Social And Behavioral Sciences, 199, 387-393. http://dx.doi.org/10.1016/j.sbspro.2015.07.523 [↗](https://doi.org/10.1016/j.sbspro.2015.07.523)
17. Neri, A., Mich, O., Gerosa, M., & Giuliani, D. (2008). The effectiveness of computer assisted pronunciation training for foreign language learning by children. Computer Assisted Language Learning, 21(5), 393-408. http://dx.doi.org/10. 1080/09588220802447651 [↗](https://doi.org/10.1080/09588220802447651)
18. Oppenheim, J. & Magnasco, M. (2013). Human Time-Frequency Acuity Beats the Fourier Uncertainty Principle. Physical Review Letters, 110(4). http://dx.doi.org/10.1103/physrevlett.110.044301 [↗](https://doi.org/10.1103/physrevlett.110.044301)
19. O’Grady, W. & Dobrovolosky, M. (1997). Contemporary Linguistics (3rd ed., pp. 40-87). Boston: Bedford/St. Martin’s. [find](https://scholar.google.com/scholar?q=O%E2%80%99Grady%2C+W%2E+%26+Dobrovolosky%2C+M%2E+%281997%29%2E+Contemporary+Linguistics+%283rd+ed%2E%2C+pp%2E+40%2D87%29%2E+Boston%3A+Bedford%2FSt%2E+Martin%E2%80%99s%2E)
20. Prica, B. & Ilić, S. (2010). Recognition of vowels in continuous speech by using formants. Facta Universitatis - Series: Electronics And Energetics, 23(3), 379-393. http://dx.doi.org/10.2298/fuee1003379p [↗](https://doi.org/10.2298/fuee1003379p)
21. Rasilo, H. & Räsänen, O. (2017). An online model for vowel imitation learning. Speech Communication, 86, 1-23. http://dx.doi.org/10.1016/j.specom. 2016.10.010 [↗](https://doi.org/10.1016/j.specom)
22. Rosen, S. & Howell, P. (2011). Signals and systems for speech and hearing (2nd ed., p. 163). London: Academic Press. [find](https://scholar.google.com/scholar?q=Rosen%2C+S%2E+%26+Howell%2C+P%2E+%282011%29%2E+Signals+and+systems+for+speech+and+hearing+%282nd+ed%2E%2C+p%2E+163%29%2E+London%3A+Academic+Press%2E)
23. Sethna, J. (2006). Statistical mechanics: Entropy, Order Parameters and Complexity (1st ed., p. 299). Oxford: Oxford University Press. [find](https://scholar.google.com/scholar?q=Sethna%2C+J%2E+%282006%29%2E+Statistical+mechanics%3A+Entropy%2C+Order+Parameters+and+Complexity+%281st+ed%2E%2C+p%2E+299%29%2E+Oxford%3A+Oxford+University+Press%2E)
24. Tan, P., Steinbach, M., & Kumar, V. (2005). Introduction to Data Mining (1st ed., p. 150). Boston: Pearson. [find](https://scholar.google.com/scholar?q=Tan%2C+P%2E%2C+Steinbach%2C+M%2E%2C+%26+Kumar%2C+V%2E+%282005%29%2E+Introduction+to+Data+Mining+%281st+ed%2E%2C+p%2E+150%29%2E+Boston%3A+Pearson%2E)
25. Wei, S., Hu, G., Hu, Y., & Wang, R. (2009). A new method for mispronunciation detection using Support Vector Machine based on Pronunciation Space Models. Speech Communication, 51(10), 896-905. http://dx.doi.org/10.1016/ j.specom.2009.03.004 [↗](https://doi.org/10.1016/j.specom.2009.03.004)
26. Wellekens, C. (1998). Introduction to Speech Recognition Using Neural Networks. Bruges: ESANN. Retrieved from [https://www.elen.ucl.ac.be/Proceedings/](https://www.elen.ucl.ac.be/Proceedings/) esann/esannpdf/es1998-456.pdf [↗](https://www.elen.ucl.ac.be/Proceedings/)
27. Zegers, P. (1998). Speech Recognition Using Neural Networks (Master of Science with a major in Electrical Engineering). University of Arizona. [find](https://scholar.google.com/scholar?q=Zegers%2C+P%2E+%281998%29%2E+Speech+Recognition+Using+Neural+Networks+%28Master+of+Science+with+a+major+in+Electrical+Engineering%29%2E+University+of+Arizona%2E)

*Updated 24 October 2026*