Misplaced Pages

Automatic pronunciation assessment is the use of speech recognition to verify the correctness of pronounced speech, as distinguished from manual assessment by an instructor or proctor. Also called speech verification, pronunciation evaluation, and pronunciation scoring, the main application of this technology is computer-aided pronunciation teaching (CAPT) when combined with computer-aided instruction for computer-assisted language learning (CALL), speech remediation, or accent reduction.

Pronunciation assessment does not determine unknown speech (as in dictation or automatic transcription) but instead, knowing the expected word(s) in advance, it attempts to verify the correctness of the learner's pronunciation and ideally their intelligibility to listeners, sometimes along with often inconsequential prosody such as intonation, pitch, tempo, rhythm, and syllable and word stress. Pronunciation assessment is also used in reading tutoring, for example in products such as Microsoft Teams and from Amira Learning. Automatic pronunciation assessment can also be used to help diagnose and treat speech disorders such as apraxia.

Intelligibility

The earliest work on pronunciation assessment avoided measuring genuine listener intelligibility, a shortcoming corrected in 2011 at the Toyohashi University of Technology, and included in the Versant high-stakes English fluency assessment from Pearson and mobile apps from 17zuoye Education & Technology, but still missing in 2023 products from Google Search, Microsoft, Educational Testing Service, Speechace, and ELSA. Assessing authentic listener intelligibility is essential for avoiding inaccuracies from accent bias, especially in high-stakes assessments; from words with multiple correct pronunciations; and from phoneme coding errors in machine-readable pronunciation dictionaries. In the Common European Framework of Reference for Languages (CEFR) assessment criteria for "overall phonological control", intelligibility outweighs formally correct pronunciation at all levels.

In 2022, researchers found that some newer speech to text systems, based on end-to-end reinforcement learning to map audio signals directly into words, produce word and phrase confidence scores closely correlated with genuine listener intelligibility. In 2023, others were able to assess intelligibility using dynamic time warping based distance from Wav2Vec2 representation of good speech.

Evaluation

Although there are as yet no industry-standard benchmarks for evaluating pronunciation assessment accuracy, researchers occasionally release evaluation speech corpuses for others to use for improving assessment quality. Such evaluation databases often emphasize formally unaccented pronunciation to the exclusion of genuine intelligibility evident from blinded listener transcriptions.

Recent developments

Some promising areas for improvement being developed in 2024 include articulatory feature extraction and transfer learning to suppress unnecessary corrections. Other interesting advances under development include "augmented reality" interfaces for mobile devices using optical character recognition to provide pronunciation training on text found in user environments. As of mid-2024, audio multimodal large language models have been used to assess pronunciation.

See also

• Phonetics
• Speech segmentation — often called "forced alignment" (of audio to its expected phonemes) in this context
• Statistical classification

References

1. El Kheir, Yassine; et al. (October 21, 2023), Automatic Pronunciation Assessment — A Review, Conference on Empirical Methods in Natural Language Processing, arXiv:2310.13974, S2CID 264426545
2. Ehsani, Farzad; Knodt, Eva (July 1998). "Speech technology in computer-aided language learning: Strengths and limitations of a new CALL paradigm". Language Learning & Technology. 2 (1). University of Hawaii National Foreign Language Resource Center; Michigan State University Center for Language Education and Research: 54–73. Retrieved 11 February 2023.
3. Isaacs, Talia; Harding, Luke (July 2017). "Pronunciation assessment". Language Teaching. 50 (3): 347–366. doi:10.1017/S0261444817000118. ISSN 0261-4448. S2CID 209353525.
4. Loukina, Anastassia; et al. (September 6, 2015), "Pronunciation accuracy and intelligibility of non-native speech" (PDF), INTERSPEECH 2015, Dresden, Germany: International Speech Communication Association, pp. 1917–1921, only 16% of the variability in word-level intelligibility can be explained by the presence of obvious mispronunciations.
5. ^ O’Brien, Mary Grantham; et al. (31 December 2018). "Directions for the future of technology in pronunciation research and teaching". Journal of Second Language Pronunciation. 4 (2): 182–207. doi:10.1075/jslp.17001.obr. hdl:2066/199273. ISSN 2215-1931. S2CID 86440885. pronunciation researchers are primarily interested in improving L2 learners' intelligibility and comprehensibility, but they have not yet collected sufficient amounts of representative and reliable data (speech recordings with corresponding annotations and judgments) indicating which errors affect these speech dimensions and which do not. These data are essential to train ASR algorithms to assess L2 learners' intelligibility.
6. Eskenazi, Maxine (January 1999). "Using automatic speech processing for foreign language pronunciation tutoring: Some issues and a prototype". Language Learning & Technology. 2 (2): 62–76. Retrieved 11 February 2023.
7. Tholfsen, Mike (9 February 2023). "Reading Coach in Immersive Reader plus new features coming to Reading Progress in Microsoft Teams". Techcommunity Education Blog. Microsoft. Retrieved 12 February 2023.
8. Banerji, Olina (7 March 2023). "Schools Are Using Voice Technology to Teach Reading. Is It Helping?". EdSurge News. Retrieved 7 March 2023.
9. Hair, Adam; et al. (19 June 2018). "Apraxia world: A speech therapy game for children with speech sound disorders". Proceedings of the 17th ACM Conference on Interaction Design and Children (PDF). pp. 119–131. doi:10.1145/3202185.3202733. ISBN 9781450351522. S2CID 13790002.
10. Bernstein, Jared; et al. (November 18, 1990), "Automatic Evaluation and Training in English Pronunciation" (PDF), First International Conference on Spoken Language Processing (ICSLP 90), Kobe, Japan: International Speech Communication Association, pp. 1185–1188, retrieved 11 February 2023, listeners differ considerably in their ability to predict unintelligible words.... Thus, it seems the quality rating is a more desirable... automatic-grading score. (Section 2.2.2.)
11. Hiroshi, Kibishi; Nakagawa, Seiichi (August 28, 2011), "New feature parameters for pronunciation evaluation in English presentations at international conferences" (PDF), INTERSPEECH 2011, Florence, Italy: International Speech Communication Association, pp. 1149–1152, retrieved 11 February 2023, we investigated the relationship between pronunciation score / intelligibility and various acoustic measures, and then combined these measures.... As far as we know, the automatic estimation of intelligibility has not yet been studied.
12. Bonk, Bill (25 August 2020). "New innovations in assessment: Versant's Intelligibility Index score". Resources for English Language Learners and Teachers. Pearson English. Archived from the original on 2023-01-27. Retrieved 11 February 2023. you don't need a perfect accent, grammar, or vocabulary to be understandable. In reality, you just need to be understandable with little effort by listeners.
13. Gao, Yuan; et al. (May 25, 2018), "Spoken English Intelligibility Remediation with PocketSphinx Alignment and Feature Extraction Improves Substantially over the State of the Art", 2nd IEEE Advanced Information Management, Communication, Electronic and Automation Control Conference (IMCEC 2018), pp. 924–927, arXiv:1709.01713, doi:10.1109/IMCEC.2018.8469649, ISBN 978-1-5386-1803-5, S2CID 31125681
14. Snir, Tal (14 November 2019). "How do you pronounce quokka? Practice with Search". The Keyword. Google. Retrieved 11 February 2023.
15. "Pronunciation assessment tool". Azure Cognitive Services Speech Studio. Microsoft. Retrieved 11 February 2023.
16. Chen, Lei; et al. (December 2018). Automated Scoring of Nonnative Speech: Using the SpeechRater v. 5.0 Engine. ETS Research Report Series. Vol. 2018. Princeton, NJ: Educational Testing Service. pp. 1–31. doi:10.1002/ets2.12198. ISSN 2330-8516. S2CID 69925114. Retrieved 11 February 2023.
17. Alnafisah, Mutleb (September 2022), "Technology Review: Speechace", Proceedings of the 12th Pronunciation in Second Language Learning and Teaching Conference (Virtual PSLLT), no. 40, vol. 12, St. Catharines, Ontario, ISSN 2380-9566, retrieved 14 February 2023{{citation}}: CS1 maint: location missing publisher (link)
18. Gorham, Jon; et al. (March 10, 2022). Speech Recognition for English Language Learning (video). Technology in Language Teaching and Learning. Education Solutions. Retrieved 2023-02-14.
19. "Computer says no: Irish vet fails oral English test needed to stay in Australia". The Guardian. Australian Associated Press. 8 August 2017. Retrieved 12 February 2023.
20. Ferrier, Tracey (9 August 2017). "Australian ex-news reader with English degree fails robot's English test". The Sydney Morning Herald. Retrieved 12 February 2023.
21. Main, Ed; Watson, Richard (9 February 2022). "The English test that ruined thousands of lives". BBC News. Retrieved 12 February 2023.
22. Joyce, Katy Spratte (January 24, 2023). "13 Words That Can Be Pronounced Two Ways". Reader's Digest. Retrieved 23 February 2023.
23. E.g., CMUDICT, "The CMU Pronouncing Dictionary". www.speech.cs.cmu.edu. Retrieved 15 February 2023. Compare "four" given as "F AO R" with the vowel AO as in "caught," to "row" given as "R OW" with the vowel OW as in "oat."
24. Common European framework of reference for languages learning, teaching, assessment: Companion volume with new descriptors. Language Policy Programme, Education Policy Division, Education Department, Council of Europe. February 2018. p. 136. OCLC 1090351600.
25. Tu, Zehai; Ma, Ning; Barker, Jon (2022). "Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility Prediction" (PDF). Proc. Interspeech 2022. INTERSPEECH 2022. ISCA. pp. 3493–3497. doi:10.21437/Interspeech.2022-10408. Retrieved 17 December 2023.
26. Anand, Nayan; Sirigiraju, Meenakshi; Yarra, Chiranjeevi (15 June 2023). "Unsupervised speech intelligibility assessment with utterance level alignment distance between teacher and learner Wav2Vec-2.0 representations". arXiv:2306.08845 .
27. Vidal, Jazmín; et al. (15 September 2019), "EpaDB: A Database for Development of Pronunciation Assessment Systems" (PDF), Interspeech 2019, pp. 589–593, doi:10.21437/Interspeech.2019-1839, hdl:11336/161618, S2CID 202742421, retrieved 19 February 2023; database .zip file.
28. Zhang, Junbo; et al. (30 August 2021), "speechocean762: An Open-Source Non-Native English Speech Corpus for Pronunciation Assessment" (PDF), Interspeech 2021, pp. 3710–3714, arXiv:2104.01378, doi:10.21437/Interspeech.2021-1259, S2CID 233025050, retrieved 19 February 2023; GitHub corpus repository.
29. Wu, Peter; et al. (14 February 2023), "Speaker-Independent Acoustic-to-Articulatory Speech Inversion", arXiv:2302.06774
30. Cho, Cheol Jun; Mohamed, Abdelrahman; Black, Alan W.; Anumanchipalli, Gopala K. (16 January 2024). "Self-Supervised Models of Speech Infer Universal Articulatory Kinematics". arXiv:2310.10788 .
31. Mallela, Jhansi; Aluru, Sai Harshitha; Yarra, Chiranjeevi (28 February 2024). Exploring the Use of Self-Supervised Representations for Automatic Syllable Stress Detection. National Conference on Communications. Chennai, India. pp. 1–6. doi:10.1109/NCC60321.2024.10486028. Retrieved 10 June 2024.
32. Sancinetti, Marcelo; et al. (23 May 2022). "A Transfer Learning Approach for Pronunciation Scoring". ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 6812–6816. arXiv:2111.00976. doi:10.1109/ICASSP43922.2022.9747727. ISBN 978-1-6654-0540-9. S2CID 249437375.
33. Che Dalim, Che Samihah; et al. (February 2020). "Using augmented reality with speech input for non-native children's language learning" (PDF). International Journal of Human-Computer Studies. 134: 44–64. doi:10.1016/j.ijhcs.2019.10.002. S2CID 208098513. Retrieved 28 February 2023.
34. Tolba, Rahma M.; et al. (2023). "Mobile Augmented Reality for Learning Phonetics: A Review (2012–2022)". Extended Reality and Metaverse. Springer Proceedings in Business and Economics. Springer International Publishing: 87–98. doi:10.1007/978-3-031-25390-4_7. ISBN 978-3-031-25389-8. Retrieved 28 February 2023.
35. Fu, Kaiqi; Peng, Linkai; Yang, Nan; Zhou, Shuran (18 July 2024). "Pronunciation Assessment with Multi-modal Large Language Models". arXiv:2407.09209 .
36. Mathad, Vikram C.; et al. (2021). "The Impact of Forced-Alignment Errors on Automatic Pronunciation Evaluation" (PDF). 22nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2021). International Speech Communication Association. pp. 176–180. doi:10.21437/interspeech.2021-1403. ISBN 9781713836902. S2CID 239694157. Retrieved 10 March 2023.

External links

• International Speech Communication Association (ISCA) Special Interest Group on Speech and Language Technologies in Education (SLaTE)

• Educational technology
• Language learning software
• Natural language processing
• Phonetics
• Speech recognition
• Statistical classification