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Overcoming Accent and Language Barriers in AI Interviews

Do AI voice interviews embrace linguistic diversity, or do they create new barriers? What the research says.

Farah MitchellFarah Mitchell·
Accent and language barriers in AI interviews visual

AI voice interview technologies are emerging with the promise of embracing linguistic diversity in the global labor market. However, the question of how different accents and regional dialects are interpreted by algorithms remains a critical debate in terms of "equal opportunity" and "fairness" principles in hiring. Current research shows that while this technology enhances accessibility on one hand, it can create new barriers for some candidates on the other.

Technological progress: Accent recognition capacity

A study conducted by Venkanna and team (2025) indicates that advanced speech recognition technologies increase accessibility by converting different accents and speech variations into text with high accuracy. Leybzon and colleagues (2025) demonstrate through real-world testing that voice AI agents can successfully interpret accented speech that even human reviewers struggle to understand. This shows that AI's technical potential to overcome language barriers is growing stronger by the day.

Sahu (2025) predicts that interview preparation tools will fully integrate multilingual support in the future, making it easier for candidates whose native language differs to interview in their own language or with their own accent. However, this technological optimism does not always align with the psychological experience of candidates.

Linguistic anxiety and pressure on candidates

Sunil's (2024) research highlights that in multilingual societies such as India, candidates experience significant stress and anxiety from the fear of being misunderstood by AI. The "Alina" case shared in the University of Sussex (2025) report documents how an accented candidate, upon learning that her words would be analyzed by AI, felt an overwhelming sense of self-consciousness and lost her natural flow. These candidates focus solely on "not making mistakes" during the interview, rather than demonstrating their energy and passion.

As Sunil (2024) points out, when cultural communication styles and social cues are not fully encoded into algorithms, AI can misinterpret candidates' qualifications. This proves that the language barrier is not merely a technical comprehension problem, but also a psychological obstacle that suppresses candidate performance.

The fairness principle: Algorithmic bias risk

Jaser and colleagues (2025) issue serious warnings that voice interview platforms' risk of misinterpreting candidates with accents or different backgrounds could undermine hiring fairness. They note that AI sometimes fails to recognize answers given in different languages to the same questions and can make incorrect personality inferences from candidates' intonation. These kinds of technical limitations carry the risk of "accent-based discrimination" rather than a merit-based evaluation.

Research argues that to overcome these issues, companies must transparently explain to candidates what the AI measures, in which language, and by what criteria. When candidates believe that algorithms cannot recognize their "unique qualities" and linguistic subtleties, their trust in the process erodes rapidly.

Conclusion: Transparent design for inclusivity

The data shows that for AI to be truly inclusive in voice interviews, merely "understanding the words" is not enough; it must also avoid treating cultural and linguistic differences as "errors." The "glass box" approach recommended in the University of Sussex (2025) toolkit demonstrates that providing candidates with clear information about the interview format reduces linguistic disadvantages.

References

  • Geiecke, F., & Jaravel, X. (2026). Conversations at Scale: Robust AI-led Interviews. LSE & CEPR.
  • Jaser, Z., et al. (2025). Artificial Intelligence (AI) in the job interview process: Toolkit for employers, careers advisers and hiring platforms. University of Sussex.
  • Leybzon, D. D., et al. (2025). AI Telephone Surveying: Automating Quantitative Data Collection with an AI Interviewer. VKL Research & SSRS.
  • Sahu, A., et al. (2025). AI Interviewer Using Generative AI. ICAAAI 2025.
  • Savani, K., et al. (2022). Applicants' Fairness Perceptions of Algorithm-driven Hiring Procedures. IMD & NUS.
  • Sunil, A. (2024). Exploring Job Applicants' Perspectives on Ai-Driven Interviews. IJAEM.
  • Tuffaha, M. (2025). Adoption Factors of Artificial intelligence in Human Resource Management. Universitat Politècnica de València.
  • Venkanna, G., et al. (2025). AI Interview Simulator: An Intelligent Hiring & Preparation Assistant. ICCSCE 2025.