Speak into a phone and an app can render your words in another language in seconds. AI-powered speech translation can be fast, convenient and fluent. But fluency does not guarantee that people will understand one another.
Authors
- Natalia Rodríguez Vicente
Lecturer in Translation & Interpreting Studies, Department of Language and Linguistics, University of Essex
- Lucas N Vieira
Professor of Translation and Technology, School of Modern Languages, University of Bristol
In the most recent census , 880,000 people in England and Wales (excluding tourists and other short-term visitors) said they could not speak English well, and 161,000 not at all. That's more than a million residents who may need language support in their daily lives, including when trying to access essential public services .
The linguistic range is vast. London alone is home to more than 250 languages . Tight budgets and a shortage of interpreters for some languages can make it difficult to secure an interpreter with the right dialect and expertise for a pressing legal, health or social care matter.
Notice we say "interpreter", not translator. In professional language services, translation typically deals with written texts, while interpreting facilitates spontaneous communication between users of different spoken or sign languages , whether face-to-face, by telephone or via video.
In a conversation, each person responds to what has come before and shapes what will follow. Added meaning emerges as they hesitate, restart or refer back to earlier points. Words are only part of this exchange - tone, pace, pauses and non-verbal cues can all shape how an utterance is understood.
Imagine a doctor asking a patient if they have missed any of their treatment sessions. The patient gives a hesitant "no" - perhaps because they are embarrassed, unsure or genuinely cannot remember.
On the surface, this reply sounds simple enough, whether the patient says nie in Polish, hayır in Turkish or hapana in Swahili. But would an AI-powered translation device spot the subtle inference in the hesitation?
AI systems work best when conditions are relatively stable: speech is clear, speakers take turns, and people say exactly what they mean. But human interaction is rarely so tidy.
In contrast, a professional interpreter would almost certainly understand the full meaning and context of this answer. They would preserve the hesitation, flag any ambiguities, and allow doctor and patient to repair a possible misunderstanding before it has any serious consequences.
As researchers in the field of public service interpreting, we are therefore concerned that any increase in adoption of AI translation services is done with due consideration for the skills and sensitivities that could be lost in the process.
Complexities of human interaction
Automated speech translation can offer some practical advantages, including on speed and availability. A 2024 survey of more than 2,500 workers across UK health and social care, legal and emergency services and the police found that a third of them had used machine translation at work.
Such systems use a process in which speech recognition converts verbal communication to text, machine translation renders it in another language, then text-to-speech software produces spoken output in the listener's tongue.
Yet the appeal of instant solutions can obscure the complexities of human interaction, and the potentially serious consequences for those involved if communication goes wrong. In an asylum interview or a Mental Health Act assessment , for example, a language-related error could significantly affect a person's health, legal status or liberty.
One approach is to reserve automated speech translation for apparently straightforward interactions. However, even a routine exchange is rarely low risk in some public services: an appointment booking may reveal alarming symptoms; a housing enquiry may uncover domestic abuse; a casual conversation with a social worker may raise a safeguarding concern.
We therefore believe technology should be introduced according to the risks of the specific interaction, with human oversight and a plan for escalation if the level of risk changes.
At the same time, human involvement does not automatically guarantee quality. Good interpreting depends on qualified professionals, effective procurement and public-service staff who know how to work with interpreters. So any responsible approach to language access means investing in both professional expertise and the systems that support it.
A hybrid future?
AI speech translation is likely to become ever-more accurate, fluent and useful. It could support qualified interpreters and, when none is immediately available, provide a temporary fallback. Ruling out its use altogether is both unrealistic and undesirable.
The more constructive questions concern when and where it is appropriate; who should supervise its use; when a human interpreter is essential; and who should be held accountable when an AI system causes harm.
It is also important to be realistic about how these systems may perform amid growing linguistic diversity. Some languages, dialects and accents are much better represented in training and test data than others. Research has documented systematic inequalities in language-technology performance worldwide as well as disparities in speech-recognition error rates within a single language.
The risks in public services are therefore uneven. Output for under-represented languages and varieties may be less accurate - and these are often the same languages for which expert interpreters may be unavailable .
As society learns to live with AI, it is important to cultivate communicative environments where technology supports and operates under human oversight, rather than replacing human expertise.
However fluent technology becomes, communication is never simply the transfer of words. Meaning emerges as an interaction unfolds: through what people say, how they say it, what they leave unsaid - and, crucially, their ability to repair misunderstandings.
These are distinctly human capabilities. Any responsible use of AI should strengthen rather than displace them.
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Lucas N Vieira receives funding from UK Research and Innovation. He is the author of Translate: Multilingual Access to Critical Services in the Age of AI (Cambridge University Press, May 2026).
Natalia Rodríguez Vicente does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
