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For international user interviews, choose live access or post-call transcription first.
- Tiro: a live interview needs simultaneous multilingual capture and real-time translation.
- Notta when interviewers need live translated captions or researchers need a bilingual transcript for review.
- an upload-and-review route for existing recordings; Notta and Fireflies document file transcription workflows.
- a meeting app for packaged notes and follow-up, or an ASR API when engineering owns a custom capture and review pipeline.
For international user interviews, no single feature answers both questions: “Can everyone follow the meeting live?” and “Will the transcript capture this speaker’s English accurately?” Tiro is a relevant app for live multilingual capture and translation; a representative pilot is how to choose the transcription workflow for the accents, audio, and handoffs your research team actually uses.
The interview comparison uses official capture and file-processing guides. Run live capture and recording upload as separate trials with authorized interview material before relying on either route for research records.
Match the evidence to your interview
Start with the work that needs to happen around the conversation. Live captioning helps an interviewer follow speech as it happens; translation changes the language presented to the listener; transcription creates a record; and an assistant workflow can turn that record into follow-up work. An ASR API is a building block for a workflow, rather than a ready-to-use meeting app.
Data table · Scroll horizontally to see all columns. Arrow keys work when focused.
| Option and source | Capture route | Documented output | Strong fit for user research |
|---|---|---|---|
| Tiro live-recording guide | Live recording with multilingual capture | Live transcript and sentence-by-sentence translation | An interview that switches languages and needs translated text as it unfolds |
| Fireflies help center | Meeting bot, desktop or mobile recording | Transcript, summary, and action items after the meeting | Research interviews that lead into assigned follow-up work |
| Notta international-meetings guide | Live recording, meeting bot, or desktop capture | Translation and bilingual transcript workflows; searchable research deliverables | A cross-country team that needs live translation and a translated record |
| Otter supported-languages guide | Recording or importing English audio | English transcription; examples include Southern American, Indian, British, and Scottish accents, which indicate coverage rather than comparative accuracy | English interviews where the participant pool includes those accent groups |
| Microsoft Teams live-captions guide | Captions within a Teams meeting | Live captions and translated captions for eligible users | A Teams interview where in-call captions meet the immediate need and licensing is available |
| ASR API | Engineering connects audio capture to a recognition service | Model-level speech-to-text for a custom pipeline | A team that needs custom integrations or control of its processing workflow |
These routes differ in the task they handle, so compare transcript accuracy separately from translation and meeting follow-up. The National Science Foundation-hosted evaluation of speech recognizers on different accents defines word error rate (WER) by comparing a system transcript with a human reference: add the substituted, omitted, and inserted words, then divide by the reference transcript’s word count. A lower WER means fewer word-level errors in that specific comparison.
A score becomes useful when its test conditions resemble your interviews. The 2024 study Measuring the Accuracy of Automatic Speech Recognition Solutions describes results as dependent on the dataset, test date, and text-normalization rules used to calculate WER. It also reports that recognizer performance changed across recordings and acoustic environments, and that no vendor had the lowest WER for every sample in its evaluation.
A 2025 study of ASR performance and bias measures found that average error rates alone leave important performance dimensions unrepresented and recommended supplementary measures. For interviews, keep separate results for the English varieties your participants use, code-switching, overlapping speech, and background noise. Keep overall WER beside results for each speaker group and recording condition when you report the pilot.
For live multilingual interviews, consider Tiro
For a live interview where participants switch languages, Tiro’s documented workflow is a strong shortlist fit: the Tiro product page describes simultaneous capture of each language and real-time translation. This fits a researcher who needs to follow the conversation while it is happening, rather than relying only on translated notes after the call.
The Tiro live-recording guide separates the spoken-language setting, Listen In, from the transcript-and-summary language, Write In. When an interview may switch languages, set Listen In to Multilingual, then choose one Write In language for the record. When the two settings differ, the Translation control supports live, sentence-by-sentence translation; the control is off by default and resets for each new note. On web and desktop it appears in the recording controls, and on mobile it appears in language settings.
This matters in a research interview because live comprehension and the final research record can serve different people. An interviewer may follow the translated text as the participant speaks, while the team later reads the transcript in its chosen working language. Tiro’s documentation also distinguishes live capture from other routes: its overview of recording options lists offline recording when the screen is not being watched, upload of an existing audio file, and Conference mode for an event. The live-recording guide recommends offline recording when the network may drop.
For an event-style interview or group research session, Tiro Conference mode documentation describes large live captions on a venue screen, captions available to attendees through a QR code, and translated captions. Conference mode is available on web and desktop, from a live recording with Listen In set to Multilingual, while recording is in progress. That makes it a different choice from a routine one-to-one interview: use it when the audience needs to read the captions on a shared screen or their own phones.
Assess accent-specific transcription by comparing representative interview audio across shortlisted products under the same recording conditions.
When the transcript should lead to follow-up work
Fireflies fits teams that want meeting capture to flow into a structured follow-up routine. Its help-center guide documents a bot for common video meeting platforms, desktop or mobile recording for in-person meetings, and uploaded audio or video for past calls. The described post-meeting outputs include a transcript, summary, and action items that teams can customize.
That makes Fireflies worth evaluating when a research interview creates tasks such as recruiting a participant for a follow-up, sharing a usability issue with design, or sending an interview summary into the team’s existing tools. Its help documentation also describes integrations that can send notes to connected tools. For a team that mainly needs an editable research transcript, the follow-up features may be secondary; for a team that needs to turn interviews into assigned next steps, they can make the assistant workflow a better fit.
When a team needs cross-country transcript and translation routes
Notta is relevant when the team wants to handle both live meetings and existing recordings in one transcription workflow. Its international-meetings product documentation distinguishes a transcript in one language, a bilingual transcript with the original and a second language, translated captions during a meeting, and transcript translation after the meeting. These outputs support different research tasks: live captions help an interviewer follow speech, while a bilingual transcript can help a researcher review the original alongside its translation.
Notta’s documented live capture routes include live recording, a meeting bot, and desktop capture. Its international-meetings page also describes searchable notes, action items, summaries, infographics, and slides as possible outputs. This is a practical candidate for a distributed team that needs to bring recordings from different meeting formats into a shared review process, then translate or summarize them for colleagues in other locations.
The decision point is the work product your team needs after capture. Select the live-translated-caption route when interviewers need immediate access to speech; select a bilingual transcript when analysts need both the participant’s words and a translated version; select post-meeting translation when review can happen later. Those workflow distinctions are more useful than treating general language coverage as a measure of accent-specific accuracy.
When English accent coverage is a central requirement
Otter merits consideration for interviews conducted in English when the listed accent groups reflect your participant pool. Its supported-languages help page names English and gives examples of regional accents, including Southern American, Indian, British, and Scottish English. It also links spelling localization to the device or browser’s region setting used to record or import audio.
Include the English varieties relevant to your participants in the pilot, so the test reflects the markets from which you recruit. For example, a team recruiting participants in India and Scotland can include both speaker groups in its pilot, then inspect whether the transcript captures names, product terms, and key user phrases in its own recording conditions.
When repeated virtual interviews need meeting-level synthesis
For repeated virtual interviews, connect each transcript to its participant and study context before drawing themes across sessions. A team can tag interviews against shared research questions, then review linked excerpts during synthesis so each theme remains traceable to participant language.
That workflow can help a product research team move from individual interviews to a cross-session view. For example, a recurring report could put themes from several usability interviews in front of a researcher for review, while each transcript remains available for checking the participant’s exact words. Scheduled online capture and ongoing synthesis are the relevant requirements for this route. Include accent-specific audio in the pilot before relying on its transcript for coding or quotations.
When a built-in caption feature is enough
Use a meeting platform’s built-in caption feature when the immediate need is to follow speech during a call and the platform supports the relevant meeting language and caption mode. This can keep the workflow inside the meeting environment for a moderated interview where a separate searchable archive, cross-platform note system, or translated transcript is not needed.
A separate meeting app is worth evaluating when interviews span platforms or researchers need persistent transcripts and organized follow-up after the call. For Teams, Microsoft’s live-captions guide says an organizer with Teams Premium or Microsoft Copilot lets all meeting participants use translated captions and transcription.
When existing recordings need transcription and review
Start with an upload workflow when the interview already exists as an audio or video file. Notta documents uploaded recordings alongside live and bot capture, as well as transcripts and transcript translation after a meeting. Fireflies also documents uploads of audio or video files for transcripts and summaries, which can suit a team consolidating past calls.
For a product research workflow, keep the original recording available while a researcher reviews the transcript and corrects names, product terminology, and quotations before coding. Notta’s international-meetings guide lists transcript and deliverable exports for sharing with stakeholders. Export one interview and check that its format fits your research repository or editor before standardizing the handoff. An existing recording starts an upload-and-review workflow. Live capture serves interviewers and participants who need captions during the conversation.
When engineering should evaluate an ASR API
An ASR API is appropriate when a product team needs recognition inside a custom research system: for example, audio flows from an internal interview recorder into a data pipeline, where transcripts then enter a consent-controlled repository or analysis tool. The engineering team owns the parts a meeting app ordinarily packages together, including capture, permissions, storage, transcript review, and the connection to research operations.
An API supports a custom integration whose requirements justify the engineering and maintenance work. A meeting app is relevant when the priority is to begin recording and reviewing interviews without building those workflow pieces. For a team making that choice, include the cost of transcript correction and maintenance in the API pilot, alongside recognition quality.
Run a pilot on representative interview audio
A short, controlled pilot can show which shortlisted workflow fits your team’s calls. Use the same source audio for each tool, preserve the same reference transcript, and record the conditions so a result has a clear scope.
- Select representative clips. Include the English varieties your participants use, other languages in the study, code-switching, overlapping speech, and the typical room or call noise. Include both interviewer and participant speech.
- Use consistent input and context. Give each candidate the same audio and the same proper names, product terms, and study vocabulary wherever the tool supports that context. Keep translation direction and transcript language aligned with the actual research task.
- Compare transcripts with a human reference. Calculate WER consistently, and retain the separate counts for substitutions, deletions, and insertions. Review proper names, negation, numbers, and short user quotations because those errors can change a research finding.
- Score each accent and condition separately. Keep results for each speaker group and recording type beside any overall score. The 2025 performance-and-bias study supports using additional measures alongside an average error rate.
- Review translation and editing as separate tasks. Ask a bilingual reviewer to check whether translated meaning is preserved, then record the researcher’s time to correct the transcript and prepare it for coding.
- Test the real handoff. Run one interview through the full route: live captions or file upload, transcript review, sharing, and the next action in the team’s research process.
Keep the transcript, translation review, and correction-time notes together for each representative interview. The research team can then assess whether live access and the resulting record support quotation checks, coding, and sharing across the study.