Voice assistant comprehension of Cameroonian English: Technological linguistic imperialism in the digital age
Keywords:
accent discrimination, automatic speech recognition, Cameroonian English, linguistic imperialism, technological bias, voice assistants, World EnglishesAbstract
This study investigates the accuracy of mainstream voice assistants (Siri, Google Assistant, and Alexa) in comprehending Cameroonian English, a variety spoken by over 8 million speakers. Through experimental phonetics and systematic error analysis involving 15 Cameroonian English speakers performing 2,250 voice commands across three platforms, the study demonstrates significantly lower comprehension rates for Cameroonian English (56.8% accuracy) compared to documented performance with Standard American and British English (95% accuracy). Phonological features including syllable-timed rhythm, consonant cluster simplification, and distinctive vowel realisations emerged as primary sources of recognition failure. Drawing on sociolinguistic theory, this research reveals how artificial intelligence technologies reproduce linguistic discrimination, creating digital exclusion for speakers of non-dominant English varieties. The paper argues that this constitutes technological linguistic imperialism that restricts equitable access to essential digital services. The study concludes with recommendations for developing inclusive automatic speech recognition systems and situates findings within broader conversations about linguistic justice in technology design.
References
Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim Code. Polity Press.
Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020). Language (technology) is power: A critical survey of “bias” in NLP. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5454–5476.
Boersma, P., & Weenink, D. (2024). Praat: Doing phonetics by computer (Version 6.3.16) [Computer software]. http://www.praat.org/
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 2(2), 77–101.
Canagarajah, S. (2013). Translingual practice: Global Englishes and cosmopolitan relations. Routledge.
Feng, S., Kudina, O., Halpern, B. M., & Scharenborg, O. (2021). Quantifying bias in automatic speech recognition. arXiv preprint arXiv:2103.15122.
Flores, N., & Rosa, J. (2015). Undoing appropriateness: Raciolinguistic ideologies and language diversity in education. Harvard Educational Review, 85(2), 149–171.
Graham, M., Hale, S. A., & Stephens, M. (2011). Geographies of the world’s knowledge. Convoco Edition.
Hecht, B., & Gergle, D. (2010). The tower of Babel meets web 2.0: User-generated content and its applications in a multilingual context. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 291–300.
Hovy, D., & Spruit, S. (2016). The social impact of natural language processing. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 591–598.
Jain, A., Upreati, M., & Jyothi, P. (2018). Improved accented speech recognition using accent embeddings and multi-task learning. Proceedings of Interspeech 2018, 2454–2458.
Kachru, B. B. (1985). Standards, codification and sociolinguistic realism: The English language in the outer circle. In R. Quirk & H. G. Widdowson (Eds.), English in the world: Teaching and learning the language and literatures (pp. 11–30). Cambridge University Press.
Koenecke, A., Nam, A., Lake, N., Nudell, J., Quartey, M., Mengesha, Z., Toups, Z., Rickford, J. R., Jurafsky, D., & Goel, S. (2020). Racial disparities in automated speech recognition. Proceedings of the National Academy of Sciences, 117(14), 7684–7689.
Korvia, A. (2013). Digital language death. PLOS ONE, 8(10), e77056.
Kouega, J. P. (2007). The language situation in Cameroon. Current Issues in Language Planning, 8(1), 3–94.
Low, E. L., Grabe, E., & Nolan, F. (2000). Quantitative characterisations of speech rhythm: Syllable-timing in Singapore English. Language and Speech, 43(4), 377–401.
Mbangwana, P. N. (2004). English patterns of usage and usages in Cameroon. Peter Lang.
Ngefac, A. (2008). Social differentiation in Cameroon English: Evidence from sociolinguistic surveys. Peter Lang.
Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. New York University Press.
Pennycook, A. (2017). The cultural politics of English as an international language. Routledge.
Phillipson, R. (1992). Linguistic imperialism. Oxford University Press.
Rosa, J., & Flores, N. (2017). Unsettling race and language: Toward a raciolinguistic perspective. Language in Society, 46(5), 621–647.
Simo Bobda, A. (1994). Aspects of Cameroon English phonology. Peter Lang.
Statista. (2024). Number of digital voice assistants in use worldwide from 2019 to 2024. https://www.statista.com/statistics/973815/worldwide-digital-voice-assistant-in-use/
Tatman, R. (2017). Gender and dialect bias in YouTube’s automatic captions. Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, 53–59.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 EPASA MOTO
This work is licensed under a Creative Commons Attribution 4.0 International License.
