Effects of Digital Communication on Segmented and Super-Segmented Features of Pronunciation Patters among Undergraduate English Language Students in Nigeria

Authors

  • MUHAMMAD Khadijah Bala Author
  • SULE, Muhammad Author

DOI:

https://doi.org/10.64348/zije.2025205

Keywords:

Digital communication, segmented, super-segmented features, University students,

Abstract

The widespread use of digital communication platforms, such as instant messaging, social media, and video conferencing, has transformed how university students interact and use language. This study investigates the effects of digital communication on pronunciation patterns, with a focus on both segmental features (vowel quality and consonant articulation) and supra segmental features (stress placement, rhythm, and intonation) among undergraduate students in Nigeria. A total of sixty participants, aged 18–25, and provided speech samples through structured reading tasks, spontaneous speech, and digital voice notes. Acoustic analyses measured vowel formants, consonant precision, stress placement, and pitch variation, while perceptual analyses involved listener ratings of intelligibility and naturalness. Results indicate that frequent engagement in digital communication is associated with reduced articulation precision, weakened stress contrasts, and a narrower pitch range, particularly in spontaneous and digital speech samples. These changes negatively impact intelligibility, suggesting that informal, rapid, and text-mediated communication encourages adaptations that deviate from standard pronunciation norms. Nevertheless, exposure to varied speech contexts can mitigate some comprehension challenges. The findings have important implications for language teaching, pronunciation training, and digital literacy programmes, highlighting the need to balance informal digital speech habits with strategies that preserve clear and intelligible communication. By understanding how digital communication influences pronunciation, educators and speech professionals can better support students in maintaining effective oral communication across both online and face-to-face contexts.

References

Boersma, P., & Weenink, D. (2021). Praat: Doing phonetics by computer (Version 6.3.85) [Computer software]. http://www.praat.org

Cao, M., Pavlik, P. I., Jr., & Bidelman, G. M. (2024). Enhancing lexical tone learning for second language speakers: Effects of acoustic properties in Mandarin tone perception. Frontiers in Psychology, 15, 1403816. https://doi.org/10.3389/fpsyg.2024.1403816 DOI: https://doi.org/10.3389/fpsyg.2024.1403816

Choi, K., So, W., & Lam, A. (2017). Lexical tone and stress perception in Cantonese-speaking ESL children. Journal of Second Language Pronunciation, 3(1), 45–62. https://doi.org/10.1075/jslp.3.1.03cho

Crystal, D. (2008). Txtng: The gr8 db8. Oxford University Press.

De la Fuente, A., & Jurafsky, D. (2024). A layer-wise analysis of Mandarin and English suprasegmentals in self-supervised speech models. arXiv. https://arxiv.org/abs/2408.13678 DOI: https://doi.org/10.21437/Interspeech.2024-2341

Dossou, B. F. P. (2023). Addressing African-accented English in speech technology: Opportunities for ASR systems. Computational Linguistics and Speech Processing Journal, 41(2), 122–139.

Hao, L., Gong, Q., & Zhang, J. (2023). The effect of stress on Mandarin tonal perception in continuous speech for Spanish- speaking learners. Interspeech 2023, ISCA Archive. DOI: https://doi.org/10.21437/Interspeech.2023-1662

Kumari, S. S., & Kumar, D. H. N. (2024). Intelligibility vs. accessibility of spoken English: A phonetic study. Migration Letters, 21(52), 1643–1657.

Ladefoged, P., & Johnson, K. (2014). A course in phonetics (7th ed.). Cengage Learning.

Levis, J. (2018). Pronunciation and intelligibility in L2 English. Cambridge University Press.

Lin, J., Zhang, H., & Lin, X. (2022). Prosodic transfer in English literacy skills among Chinese elementary-age students: Controlling for non-verbal intelligence. Journal of Intelligence, 10(4), 114. https://doi.org/10.3390/jintelligence10040114 DOI: https://doi.org/10.3390/jintelligence10040114

Ojochegbe, R., Tersoo, A., & Nicodemus, N. (2024). Teaching intelligibility in African English classrooms: A focus on phonological and supra segmental features. International Journal of Applied Linguistics, 34(1), 57–72. https://doi.org/10.1111/ijal.12845 DOI: https://doi.org/10.1111/ijal.12845

Senowarsito, S., & Ardini, N. N. (2023). The use of artificial intelligence to promote autonomous pronunciation learning: Segmental and supra segmental features perspective. Indonesian Journal of English Language Teaching and Applied Linguistics, 8(2). DOI: https://doi.org/10.21093/ijeltal.v8i2.1452

Tagliamonte, S. A., & Denis, D. (2020). Linguistic change in real time: Observing the evolution of language. Cambridge University Press.

Thir, V. (2023). Co-text, context, and listening proficiency as crucial variables in intelligibility among non-native users of English. Studies in Second Language Acquisition, 45, 1210–1231. https://doi.org/10.1017/S0272263123000207 DOI: https://doi.org/10.1017/S0272263123000207

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Published

2025-12-10

How to Cite

Effects of Digital Communication on Segmented and Super-Segmented Features of Pronunciation Patters among Undergraduate English Language Students in Nigeria . (2025). Federal University Gusau Faculty of Education Journal, 2(3), 214-221. https://doi.org/10.64348/zije.2025205