Can AI Transcription Accurately Handle Different Accents?

Will AI Transcribe Every Participant's Accent Equally Accurately?

Suppose you interview university teachers from different countries. Some speak American English, others use British English, and several speak English with Filipino, Indian, or other regional accents. You process all the recordings using the same AI transcription system.

The resulting transcripts may appear equally polished, but that does not mean the system recognized every speaker with equal accuracy. Some participants' statements may contain more substitutions, omissions, or incorrectly recognized expressions than others.

This raises a methodological concern: if transcription accuracy varies systematically across speakers, could the technology distort whose experiences are represented most faithfully in your research?

AI Can Transcribe Different Accents, but Accuracy Is Not Necessarily Equal

Why Accents Affect AI Transcription Accuracy

Why Does an Accent Matter to Speech Recognition?

An accent refers to patterns of pronunciation associated with a speaker's linguistic background, region, or speech community. These patterns may involve vowel and consonant production, rhythm, stress, and intonation.

Automatic speech recognition systems must identify words from acoustic signals that vary considerably across speakers. A word may be pronounced differently depending on regional speech patterns, first-language influences, or individual speaking habits.

Modern speech recognition systems can accommodate considerable variation, but their performance is influenced by the speech patterns represented in training data and the capabilities of the model.

An unfamiliar pronunciation may therefore be recognized less accurately than a pronunciation that closely resembles speech patterns well represented during training.

Accent is not a deficiency in a participant's language. The relevant issue is whether the transcription system has learned to recognize the speech variety being used.

What Does Published Research Show About Accent-Related Errors?

Several studies have documented differences in automatic speech recognition performance across accents and dialects.

Koenecke and colleagues (2020), in a study published in the Proceedings of the National Academy of Sciences, evaluated five commercial speech recognition systems using recordings from 42 white speakers and 73 Black speakers in the United States.

The researchers reported an average word error rate of 0.35 for Black speakers compared with 0.19 for white speakers across the systems evaluated. This means that the systems produced substantially more word-recognition errors for Black speakers in the study's recordings.

The researchers also examined identical phrases spoken by different speakers and found that disparities persisted, supporting the interpretation that differences in pronunciation and prosody contributed to the observed performance gap. The original study provides the full evaluation and limitations.

These findings concern specific systems and speech samples evaluated in 2020. They should not be treated as current performance figures for every AI transcription service or as evidence that all speakers within a demographic group share the same accent.

Graham and Roll (2024) evaluated OpenAI's Whisper across native and non-native English accents. Their findings showed better recognition performance for American English than for British and Australian English in the tested material, while Canadian English showed similar performance to American English. Native English accents generally produced better recognition results than non-native accents in their evaluation.

The study also found that conversational speech was more challenging than read speech. This distinction is particularly relevant to research interviews, where participants speak spontaneously rather than reading prepared sentences.

Earlier research by Tatman (2017) similarly identified differences in YouTube automatic captioning accuracy across English dialect groups, demonstrating that accent-related disparities are not unique to a single contemporary model.

Together, these studies establish that accent-related variation in speech recognition performance is a documented phenomenon. They do not establish a universal ranking of accents from easiest to hardest to transcribe.

Does an Accent Automatically Cause Poor Transcription?

No. An accent does not automatically make speech difficult to recognize. Transcription accuracy depends on the interaction between the speaker, recording conditions, and recognition system.

A system trained or adapted using representative speech from a particular linguistic community may recognize that community's pronunciation patterns effectively.

Conversely, a system may perform poorly on an accent that is underrepresented in its training or evaluation data, even when the recording is clear and the speech is readily understandable to a human listener familiar with that variety.

Researchers should therefore avoid describing particular accents as inherently unclear. A more precise description is that a particular transcription system demonstrated lower recognition accuracy for certain speech samples under specified conditions.

Which Speech Characteristics Can Influence Recognition?

Accent-related transcription performance may involve several interacting characteristics.

These characteristics are not unique to non-native speakers. All accents exhibit systematic pronunciation patterns, and conversational speech varies within every linguistic community.

Why Does Accent-Related Accuracy Matter for Qualitative Research?

In qualitative research, transcription errors may affect more than the appearance of a transcript. They can influence how participant statements are coded, interpreted, and represented in published findings.

Suppose a researcher interviews participants from several linguistic communities about their experiences with educational technology. If one group's interviews contain substantially more transcription errors, those participants' accounts may be represented less faithfully.

Some statements may become difficult to interpret, while others may be assigned incorrect meanings. Researchers may inadvertently devote more analytical attention to transcripts that are easier to read, creating an additional risk of unequal representation.

This is a potential methodological consequence rather than an inevitable outcome. Its importance depends on the error patterns and how the researcher conducts verification and analysis.

The central concern is whether differences in transcription accuracy introduce systematic distortions into the research evidence.

Can Overall Accuracy Conceal Poor Performance for Certain Accents?

Yes. An overall accuracy figure summarizes performance across the evaluated recordings and may conceal substantial differences between speakers or groups.

For example, a system might perform very well on recordings from one group but considerably less well on another. The overall average would not necessarily reveal the disparity.

Researchers should therefore examine error rates across relevant subsets of their dataset when the sample and evaluation procedure permit meaningful comparison.

However, accent-group comparisons require care. Participants within the same linguistic community may have diverse pronunciation patterns, and a small number of speakers cannot establish the performance of a system for an entire population.

It is more defensible to describe observed differences in the study's recordings than to generalize broadly about the accuracy of AI transcription for a nationality or language group.

What About Filipino English and Other Philippine Accents?

Filipino English encompasses variation in pronunciation influenced by speakers' linguistic backgrounds, educational experiences, and speech communities. It should not be treated as one uniform accent.

Researchers interviewing Filipino participants may encounter English spoken with different regional pronunciation patterns, Filipino-English code-switching, and technical terminology pronounced according to local conventions.

The studies discussed above do not establish a reliable accuracy percentage for Filipino English across contemporary transcription systems. Such a figure would require an evaluation using representative Filipino English recordings and clearly defined scoring procedures.

For a study involving participants from different Philippine regions, the practical approach is to test the selected transcription system on recordings that reflect the linguistic variation actually present in the sample.

If participants frequently switch between Filipino and English, the study should also consider the separate challenge of multilingual interview transcription.

Can Accent Recognition Be Improved?

Potentially. Improvements may come from selecting a system with stronger performance on the relevant speech variety, using better recordings, or applying supported model adaptation features.

However, not all systems allow accent-specific adaptation, and improved microphone quality cannot eliminate errors arising from limitations in a model's recognition of particular pronunciation patterns.

Researchers should distinguish between improving the acoustic quality of recordings and improving the recognition system's ability to handle linguistic variation.

When selecting a tool, testing with representative recordings is more informative than assuming that a multilingual model will necessarily recognize every English accent equally well.

Should Researchers Rewrite Accented Speech Into Standard English?

Not simply because a participant uses a different accent or language variety.

Transcription involves representing what participants said, while editorial rewriting changes how their speech appears in written form. These are different activities.

For example, if a participant uses a grammatical construction associated with their language variety, replacing it with standardized academic English may remove information about their original expression.

Whether grammatical features should be preserved depends on the study's transcription convention and analytical purpose. Researchers should avoid treating nonstandard grammatical forms as transcription mistakes merely because they differ from formal written English.

This distinction is particularly important when transcription changes could alter participant meaning or remove linguistic details relevant to interpretation.

Evaluating AI Transcription Across Different English Accents

These figures are invented solely to illustrate an evaluation procedure. They do not represent actual error rates for Filipino English or any other accent.

The results suggest that the selected system performs differently across the three subsets. However, the researcher cannot immediately conclude that accent alone caused the differences. Recording quality, speaking style, vocabulary, and other characteristics may also contribute.

The example demonstrates why an average transcription accuracy figure may be insufficient for a linguistically diverse research sample. The objective is not to rank participants' speech, but to establish whether the technology represents their contributions with adequate fidelity.

Common Misunderstandings About Accent-Related Transcription Accuracy

How Should Researchers Handle Accent Variation in Interview Transcription?

If your participants speak different varieties of a language, accent variation should be considered during transcription planning rather than discovered only after coding begins.

The most useful approach is to evaluate the actual transcription system using representative recordings and establish a review procedure that addresses observed differences in performance.

For qualitative research, manual transcript verification is particularly valuable when the system's performance varies across speakers. Reviewers should concentrate on faithfully representing what was said, not making every participant sound linguistically identical.

Accent-related evaluation also contributes to research fairness. When some participants' speech is systematically transcribed less accurately, the research team should take reasonable steps to prevent those technical differences from becoming unequal treatment of participants' evidence.

Before Using AI Transcription With Different Accents

Frequently Asked Questions About AI Transcription and Accents

Different Accents Deserve Equally Careful Transcription

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