Can AI Reliably Transcribe Research Interviews?

Can You Trust AI to Transcribe Your Research Interviews?

You have completed a series of research interviews, each lasting approximately an hour. Now comes transcription: listening to the recordings, identifying what each participant said, and converting the conversations into text suitable for analysis.

AI transcription tools promise to complete much of this work in minutes. The resulting transcripts often look remarkably polished, with punctuation, paragraphs, and even speaker labels. But can you trust them to represent what participants actually said?

The distinction matters because qualitative researchers frequently analyze the transcript rather than repeatedly returning to the original recording. An incorrectly transcribed word, omitted qualification, or misplaced speaker label may influence the interpretation of the data.

AI Can Produce Useful Transcripts, but Reliability Is Conditional

How AI Transcription Works and What Makes It Reliable

How Does AI Convert an Interview Recording Into Text?

Most contemporary AI transcription systems use automatic speech recognition (ASR), a technology that converts spoken language into written text. Modern systems commonly employ neural networks trained on speech recordings and corresponding transcripts.

When you provide an interview recording, the system processes the audio and predicts the words being spoken. Depending on its capabilities, it may also insert punctuation, divide the conversation into segments, generate timestamps, or assign speaker labels.

These additional features should not be confused with transcription accuracy. A system may correctly recognize most words while incorrectly attributing a statement to the interviewer rather than the participant.

Similarly, a transcript that reads naturally is not necessarily faithful to the recording. AI systems may produce plausible words that fit the conversational context but differ from what was actually spoken.

What Does Research Tell Us About AI Transcription?

Published research supports the practical usefulness of automated transcription, but it also documents substantial variation in performance.

A study published in the European Journal of Cardiovascular Nursing, titled Transcribing in the digital age: qualitative research practice utilizing intelligent speech recognition technology, examined the use of speech recognition in qualitative research. The researchers reported potential cost savings but also encountered transcription difficulties associated with accents, pronunciation, speech tempo, punctuation, and dialogue accuracy.

In that study, automated transcription was not consistently faster or more accurate than outsourced human transcription once the necessary corrections were considered. The findings illustrate why the time required to generate a transcript should not be confused with the time required to produce an analysis-ready transcript.

Another study, The development of an automatic speech recognition model using interview data from long-term care for older adults (2023), investigated speech recognition using interview recordings involving older adults and care-related participants. Its initial model produced a word error rate of 48.3% on interview data. After fine-tuning with relevant interview recordings, the average word error rate decreased to 24.3%.

Those figures describe a particular research setting and system, not the accuracy of all contemporary AI transcription tools. They demonstrate that performance can depend substantially on how well a speech recognition model matches the recordings it is asked to process.

The practical lesson is that AI transcription accuracy must be evaluated in relation to the actual interview conditions rather than inferred from a tool's general reputation.

What Types of Errors Can AI Transcription Produce?

Automatic transcription errors can affect individual words, entire statements, or the structure of a conversation.

Substitutions, deletions, and insertions are standard categories in automatic speech recognition evaluation. Speaker attribution and punctuation problems are additional concerns when preparing research transcripts.

Not all errors have equal analytical consequences. Misspelling a common word may be relatively harmless, while omitting a negation such as "not" could reverse the participant's expressed position.

Why Does Transcription Accuracy Vary Between Interviews?

Even when the same transcription software is used, recordings may produce noticeably different results.

Recording quality. Background conversations, poor microphone placement, reverberation, and low recording volume can make speech more difficult to recognize. Research examining automated transcription of simulated police interviews has documented differences in system performance associated with audio quality.

Accent and pronunciation. Recognition performance can vary across regional and non-native accents. Graham and Roll (2024), in their evaluation of OpenAI's Whisper, reported differences in recognition performance across English accent groups and speaker characteristics. Researchers should therefore avoid assuming that different accents will be transcribed equally accurately.

Specialized vocabulary. Technical terms, uncommon names, abbreviations, and discipline-specific expressions may be incorrectly recognized, particularly when the model has limited exposure to similar speech. This matters when transcribing specialized research terminology.

Overlapping speech. Participants and interviewers sometimes speak simultaneously. A system may omit overlapping words, combine statements, or confuse the speakers. These problems are especially relevant to interviews involving multiple speakers.

Language switching. Participants may alternate between languages within a sentence or conversation. Such code-switching can create additional recognition difficulties, particularly when the selected model is poorly suited to the languages involved. Researchers conducting multilingual interviews should test the system on representative recordings.

These factors may interact. For example, a recording containing specialized terminology, overlapping speech, and frequent language switching may be substantially more difficult to transcribe than a clear, single-speaker recording.

How Is AI Transcription Accuracy Measured?

One widely used evaluation measure is word error rate (WER). It compares an automatically generated transcript with a reference transcript and counts substitutions, deletions, and insertions.

Word error rate is useful for comparing transcription performance under defined conditions. However, it does not establish whether the errors affect analytically important statements. A low WER can coexist with a consequential misquotation.

For qualitative research, researchers should therefore consider both word-level accuracy and the preservation of meaning.

Does a Readable Transcript Mean It Is an Accurate Transcript?

No. Readability and fidelity are different properties.

A transcription system may remove repeated words, insert punctuation, or produce grammatically coherent sentences. Some tools also offer post-processing functions that rewrite or summarize spoken language.

These transformations may make a transcript easier to read, but they can also conceal hesitation, uncertainty, or the participant's original phrasing.

The appropriate balance depends on the study. A thematic analysis may not require every hesitation to be represented, whereas conversation analysis may depend on precise pauses, interruptions, and other interactional details.

Researchers should establish a transcription convention before processing the recordings, particularly when deciding whether filler words and pauses should be preserved.

Can AI Transcription Change What a Participant Appears to Mean?

Yes. A transcription error can be analytically consequential even when it affects only a small portion of the recording.

Consider the difference between "I would recommend it" and "I wouldn't recommend it." If the system misrecognizes the negative construction, the resulting transcript may communicate the opposite position.

Other errors can be more subtle. Removing "sometimes," "perhaps," or "I think" may make a tentative statement appear more certain. Incorrect punctuation can also change whether a phrase reads as a question, qualification, or assertion.

This is why errors that change participant meaning deserve attention beyond aggregate transcription accuracy.

What About Confidentiality and Data Protection?

Research interview recordings may contain names, voices, personal experiences, institutional information, and other potentially identifying details. Uploading these recordings to an external transcription service can involve transferring research data to a third-party provider.

Before using such a service, researchers should examine the provider's data processing arrangements, retention practices, access controls, and applicable contractual protections. Institutional requirements and the conditions established during informed consent must also be considered.

For some projects, a locally operated transcription system may offer greater control over where recordings are processed. However, local processing does not automatically guarantee security, particularly if the device, software, or storage arrangements are inadequately protected.

The decision to use cloud-based AI transcription for confidential interviews should be made before any participant recording is transferred.

Transcribing an Interview About Teachers' Experiences With AI

The omitted word "don't" changes the meaning of the participant's statement. Without verification, the researcher might interpret the response as evidence of confidence in AI-generated instructional materials when the participant was expressing reservations.

This example demonstrates why an apparently minor transcription error can have a disproportionate effect on qualitative interpretation. The concern is not simply whether the system recognizes most words correctly, but whether the transcript faithfully represents statements used to support the research findings.

Common Misunderstandings About AI-Generated Transcripts

How Should Researchers Decide Whether to Use AI Transcription?

AI transcription is often most useful as the first stage of transcript preparation. It can reduce the effort required to produce an initial text record, leaving researchers to concentrate on verification, contextual interpretation, and the correction of consequential errors.

Whether this approach is suitable depends on the recording conditions, the type of analysis, the sensitivity of the data, and the resources available for verification.

For a qualitative study, it is also worth deciding in advance what counts as a completed transcript. A file generated by AI is not necessarily the same as a verified transcript ready for coding.

A practical approach is to preserve the original audio, produce a preliminary transcript, verify the content according to a documented procedure, and maintain an appropriate record of corrections. The detailed steps can be established through a defensible transcript verification workflow.

Where further AI processing is planned, researchers should separately consider de-identifying transcripts before submitting them to additional AI systems.

Before Using AI-Generated Interview Transcripts for Analysis

Frequently Asked Questions About AI Research Interview Transcription

AI Can Transcribe the Interview, but Accuracy Still Needs Verification

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