AI translation and speech tools: accuracy, bias, and when to trust them

Artificial intelligence translation and speech tools are good enough, often enough, that many people no longer notice they are using them. A meeting invite arrives with auto-generated captions. A voice note becomes text. A paragraph in French is rendered into English inside a browser tab. For everyday use, this can feel like magic. For organisations, it looks like productivity: fewer hours spent typing, summarising, transcribing and translating.

The risk is that this familiarity obscures the fact that performance is still uneven. These tools are most reliable when the language is standard, the audio is clean, and the subject matter is ordinary. They become risky when you move into accents, dialects, specialist vocabulary, noisy environments, or high-stakes contexts such as healthcare, law and safeguarding. They also raise privacy questions, because voice and language data can be highly identifying, and in many deployments, it is processed by third parties.

A useful way to think about trust is not whether a tool is “accurate” in general, but whether it is accurate enough for a specific task, with a clear plan for what happens when it is wrong.

Translation and transcription are not the same problem

Machine translation and speech-to-text are often bundled together in consumer products, but the failure modes are different. Translation is about meaning across languages. Speech-to-text is about recovering words from audio. Each can distort the message in ways that are easy to miss.

Speech systems tend to fail on names, numbers, acronyms and domain terms, and they fail more often when audio quality is poor. They also struggle with overlapping speech, fast speakers and informal conversation. Translation systems may produce fluent sentences that sound confident while subtly shifting meaning, softening uncertainty, or converting hedged language into definitive statements. When a translation reads smoothly, users are less likely to question it, which can make small errors more dangerous than obvious gibberish.

Both tasks can be evaluated, but the metrics capture different things. In speech-to-text, the most common measurement is word error rate, which counts substitutions, insertions and deletions compared with a human reference transcript. NIST, the US National Institute of Standards and Technology, uses word error rate in evaluation plans for speech recognition systems, and the formula is widely adopted across the field. Translation is often evaluated with automatic metrics, but major evaluation campaigns also emphasise human judgment, because fluent output can still be wrong in ways that metrics miss. Work connected to the Workshop on Machine Translation highlights this tension, with human evaluation remaining central to reliable assessment even as automated metrics improve.

For most users, these details matter for one reason: a tool can be highly accurate on average and still fail badly for your language variety or your kind of work.

Accuracy varies by language, dialect and domain

Performance differences by language are partly about data. Systems trained on abundant, standardised text and speech typically perform better on those varieties. Less-resourced languages, regional dialects and mixed-language conversation often have less training data and fewer high-quality benchmarks. The result is not merely lower accuracy, but more unpredictable errors.

Speech recognition disparities are particularly well documented. A widely cited study in the Proceedings of the National Academy of Sciences found substantial differences in word error rate for Black and white speakers across several commercial systems, with errors almost doubling in some cases. The key point for UK users is not the specific demographic categories in that research, but the broader finding: error rates are not evenly distributed. Accents, dialects and sociolects can produce materially different results, and those differences can translate into unequal outcomes when transcripts are used for decisions, documentation or performance monitoring.

Domain also matters. In medicine, small transcription mistakes can change dosage, symptoms or allergies. In law, they can change the meaning of a clause or the intent of a statement. Even in everyday workplace settings, domain terms are where speech systems stumble, particularly with product names, industry jargon and proper nouns. A tool that is excellent at transcribing a podcast can be poor at transcribing a clinical handover or an earnings call.

Translation adds another layer. Many systems handle straightforward news prose well, but accuracy can degrade with idioms, humour, technical documents, and language that depends on context, such as pronouns in languages where gender and formality are encoded differently. The most dangerous errors are often not random words, but systematic shifts: polite requests rendered as commands, uncertainty rendered as certainty, or conditional language dropped entirely.

Bias is not only about offensive language

When people talk about bias in language tools, they often think about slurs or stereotyping. Those exist, but the more common harm in translation and transcription is unequal performance and unequal visibility.

If a system routinely mishears a particular accent, those speakers must work harder to be understood, or their contributions become less legible in written records. If translation handles a dominant language pair well but performs poorly on minority languages, those communities may face worse access to services or less accurate information. In organisations, these effects can be amplified when transcripts become searchable records and translated documents become the basis for decisions.

Bias can also appear in subtler ways, such as gendered defaults in translation, or the way politeness markers are handled when translating between languages with different social conventions. These are not merely linguistic curiosities. They shape how people are perceived.

The practical implication is that tool selection should include an explicit check for the language varieties you actually use, not just the ones product marketing highlights.

When these tools are reliable, and when they are risky

For everyday users, the reliable zone tends to look like this: short, low-stakes tasks; clean audio; one speaker at a time; common vocabulary; and content where approximate meaning is enough. Captions for a casual video call, rough notes from a meeting you attended, or translating a restaurant menu generally fall into this category.

Risk rises when you need precision. Anything involving medical advice, legal commitments, safeguarding, financial instructions or employment decisions deserves a different standard. The question becomes whether the tool output is treated as a draft that a competent human reviews, or as a definitive record.

Vendors sometimes offer informal benchmarks, such as describing a word error rate range that is “good” or “acceptable”, but those thresholds are context dependent. Microsoft’s guidance for evaluating a custom speech model, for example, discusses word error rate and suggests that higher values indicate poorer quality and a need for improvement, while also warning that the distribution of errors matters. Microsoft Learn The more important lesson is that a single percentage does not tell you whether the errors fall on names, negations, numbers, or critical terms.

How to evaluate a tool before you depend on it

A responsible evaluation does not require a lab, but it does require structure. Start by defining what “good enough” means for your use case. A journalist transcribing an interview needs different performance from a clinician documenting symptoms, and both differ from a team generating subtitles for social media.

A practical testing plan begins with representative material. Record short audio samples that match real conditions: the microphones you will actually use, the background noise you expect, the accents and speaking styles in your organisation, and the vocabulary that matters. Do the same for translation by collecting typical documents: emails, reports, customer messages, and any templates you rely on.

Then compare system output against a human reference. For speech, that means having a person create a careful transcript of a sample, then calculating or at least estimating the kinds of errors that appear. NIST’s evaluation plans explain word error rate in terms of deletions, insertions and substitutions, which is a useful framework even if you do not compute the exact number. For translation, the key is human review focused on meaning rather than fluency. It can help to ask reviewers to answer simple questions after reading the output: what is being promised, what is uncertain, what actions are requested, and what numbers or dates are stated.

The most valuable part of testing is not a score, but an error catalogue. Note whether the system consistently mishandles names, negation, numbers, or specific terms. Look for patterns across speakers and documents. Those patterns tell you whether mitigation is possible through custom vocabularies, style guides, or process changes, or whether the tool is simply unsuitable.

Safe use for everyday users and small firms

Most people do not need formal evaluation, but they do need sensible boundaries. Treat machine translation as a fast way to get the gist, and as a drafting aid for messages that you will reread, not as a final authority on meaning. If you are sending something important, keep your original short and literal, avoid idioms, and check the translation by translating it back or asking a speaker of the target language to confirm. If the message includes numbers, dates, prices, addresses or medical details, assume it needs verification.

For speech-to-text, improve inputs before you blame outputs. Use a decent microphone, reduce background noise, and encourage one speaker at a time. If you are capturing minutes from a meeting, make it explicit that the transcript is not a verbatim legal record. For interviews, especially where quotes matter, recordings remain the source of truth, and the transcript is a tool for searching and drafting.

When the output will be shared publicly or used for decisions, a human review step should be non-negotiable. The question is not whether the tool is clever, but whether the consequences of a mistake are tolerable.

Confidentiality, consent and the data trail

Translation and transcription tools sit on a fault line between convenience and confidentiality. Speech, in particular, is personal data in many contexts, and can include special category data when it reveals health, ethnicity, or other sensitive information. Even when a system does not attempt biometric identification, voice recordings can be identified in practice.

The European Data Protection Board has issued guidance on virtual voice assistants that emphasises transparency and lawful processing, and it discusses common purposes such as executing requests, improving models and profiling. European Data Protection Board. The broader point for users is that “turning on captions” or “dictating a note” can trigger data flows beyond the device in front of you, depending on settings and providers. Organisations should assume that using third-party services means they are sending personal data to a processor, with obligations to secure it and limit it to what is necessary.

Consent matters in both ethical and legal senses. If you record and transcribe meetings, participants should know. If you use translation tools on customer messages, customers may need to be told, especially if data is sent outside your systems. If you handle sensitive material, you need to know where processing happens, how long data is retained, and whether it is used to improve models. The European Data Protection Supervisor’s orientations on generative AI make the general point that claims about not processing personal data should be tested by asking what controls and procedures are in place. The same scepticism applies to speech and translation vendors.

A practical approach is to separate convenience features from high-sensitivity work. For confidential material, prefer on-device processing where possible, or enterprise-grade deployments with clear contractual terms. If you cannot establish where data goes, assume it is unsuitable for sensitive content.

Choosing tools responsibly

Tool selection is often framed as a choice between brands, but the more important choice is between deployment models. Consumer tools optimise convenience. Enterprise tools often offer stronger governance, logging controls, and options to keep data out of training pipelines. Open-source systems can offer control, but they bring operational responsibilities, including security and maintenance.

Whichever route you choose, insist on evidence that matches your reality. If a vendor claims high accuracy, ask what languages, accents and domains were tested, and under what conditions. If a system is used for multilingual support, test minority language performance explicitly. If it will be used in regulated settings, requires clear documentation and a defined human review process.

Translation and speech tools are among the most useful applications of AI precisely because they can take mundane work off people’s hands. The safest way to use them is to keep them in that lane, as accelerators of comprehension and drafting, not as arbiters of truth, and to treat privacy as a design requirement rather than an afterthought.