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

Technological progress: The capacity to understand accents

A study conducted by Venkanna and team (2025) notes that advanced speech recognition technologies are increasing accessibility by converting different accents and speech variations to text with high accuracy. Leybzon and colleagues (2025) demonstrate that in real-world testing conditions, voice AI agents can successfully interpret accented speech that even human reviewers struggled to understand. This shows that AI's technical potential to overcome language barriers grows stronger with each passing 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 candidates' psychological experience.

Linguistic anxiety and the pressure on candidates

Sunil's (2024) research highlights that in multilingual societies like India, candidates experience significant stress and anxiety due to the fear of being misunderstood by AI. The "Alina" case shared in the University of Sussex (2025) report documents how a candidate with an accent felt excessively "self-conscious" upon learning that her words would be analyzed by AI, causing her to lose her natural flow. These candidates focus their energy during the interview solely on "avoiding mistakes" rather than showcasing their passion and enthusiasm.

As noted by Sunil (2024), when cultural communication styles and social cues are not fully incorporated into algorithms, AI can misinterpret candidates' qualifications. This proves that the language barrier is not merely a technical comprehension issue but also a psychological obstacle that suppresses a candidate's 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 diverse backgrounds can undermine hiring fairness. They note that AI sometimes fails to recognize answers given in different languages to the same questions and can draw erroneous personality inferences from a candidate's intonation. Such technical limitations bring the risk of "accent-based discrimination" rather than merit-based evaluation.

Studies argue that to overcome these issues, companies must clearly 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 nuances, 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, simply "understanding the words" is not enough — it must also refrain from treating cultural and linguistic differences as "errors." The "glass box" approach recommended in the University of Sussex (2025) guide 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.