Qualitative Research Transcription: GDPR, IRB & NVivo

Qualitative research transcription requires more than converting audio to text. Academic and clinical researchers must satisfy GDPR Article 9 data protections, IRB protocol data management requirements, and software compatibility with NVivo, MAXQDA, and ATLAS.ti — all while maintaining participant confidentiality. This guide covers each requirement in detail, including how automatic speaker diarization handles multi-participant interviews and which output format integrates with qualitative coding software.

Key Takeaways

• GDPR Article 9 classifies most qualitative research data as special category data requiring extra protections — short retention windows and minimal storage are essential
• IRB protocols typically require researchers to document transcription methods, data storage duration, and who has access — use this guide to fill that section
• BrassTranscripts deletes audio after 24 hours and transcripts after 48 hours, with no account creation or long-term storage
• Pyannote 3.1 speaker diarization labels each participant consistently throughout multi-person interviews
• JSON output includes word-level timestamps for software alignment; TXT is the primary format for NVivo, MAXQDA, and ATLAS.ti manual coding
• Two ready-to-use AI prompts below: thematic analysis and research summary generation

Quick Navigation

• What Makes Research Transcription Different?
• Is AI Transcription GDPR-Compliant for Research?
• HIPAA and the Common Rule: Health Research
• What Your IRB Protocol Needs to Know
• Speaker Identification for Multi-Participant Interviews
• NVivo, MAXQDA and ATLAS.ti: Which Format to Use?
• AI Prompt: Qualitative Research Thematic Analysis
• AI Prompt: Interview Research Summary Generator
• Step-by-Step Research Workflow

What Makes Research Transcription Different?

General-purpose transcription produces text. Research-grade transcription must produce text that is legally compliant, methodologically defensible, and immediately usable in analytical software.

The differences are significant:

This guide addresses each of these requirements directly.

Is AI Transcription GDPR-Compliant for Research?

AI transcription can be GDPR-compliant for qualitative research, provided you select a service that implements data minimization and short retention by design. Here is what the regulation requires and how it applies.

What GDPR Article 9 Means for Research Data

GDPR Article 9 identifies special categories of personal data that receive heightened protection under the regulation. These categories include health data, racial or ethnic origin, political opinions, religious beliefs, trade union membership, genetic data, and data concerning sex life or sexual orientation.

Qualitative research interviews frequently touch these categories — health interviews, community studies, biographical research, and clinical studies almost always involve Article 9 data. Processing this data is restricted to specific lawful bases, with research purposes covered under Article 9(2)(j) ("scientific research purposes"), subject to appropriate safeguards.

The core safeguard requirement is proportionality: data collected and stored must be limited to what is strictly necessary for the research purpose.

Consult your institution's Data Protection Officer or compliance office for formal legal interpretation of GDPR requirements for your specific study.

Data Minimization: The Principle That Matters Most

GDPR Articles 5 and 25 establish data minimization and privacy by design as foundational requirements. For transcription, this means:

• Collect only what you need: Audio files are identifiable data. Once the transcript exists, continued storage of the raw audio increases your data exposure without research benefit.
• Delete promptly: Retain audio and transcripts for the minimum period needed to complete transcription verification. Not months. Not "just in case."
• Limit access: Only researchers named in the IRB protocol should have access to raw transcripts containing identifying information.
• Store securely: Transcripts stored on personal laptops or unencrypted drives create compliance exposure.

BrassTranscripts GDPR Data Minimization Checklist

No long-term storage. No account required. No data retained after the deletion window closes.

For research conducted under EU or UK GDPR, document these retention practices in your data management plan as evidence of Article 25 compliance. Your institution's DPO can advise on whether additional documentation (such as a Data Processing Agreement) is required for your specific context.

For a broader overview of AI transcription security practices, see our AI Transcription Security and Privacy Guide.

HIPAA and the Common Rule: Health Research

For US-based researchers, two regulatory frameworks apply depending on study type.

HIPAA applies to covered entities (healthcare providers, health plans, and healthcare clearinghouses) and their business associates. If your research uses Protected Health Information (PHI) from a covered entity's systems, HIPAA applies.

The Common Rule (45 CFR 46) applies to federally funded human subjects research conducted at institutions with an assurance on file with HHS. Most academic qualitative research falls under the Common Rule rather than HIPAA.

For qualitative interview studies under the Common Rule, key protections include:

• Identifiable private information: Interview audio and transcripts containing participant names, voices, or identifying details constitute identifiable private information requiring IRB oversight.
• Confidentiality protections: Researchers must implement and document protections preventing unauthorized disclosure.
• Data security: Study data must be stored securely. Cloud processing services used during transcription should be evaluated for compliance with your institution's data security standards.

Consult your Institutional Review Board and your institution's research compliance office for guidance specific to your study protocol. Requirements vary by institution, funding source, and study type.

What Your IRB Protocol Needs to Know

IRB protocols for qualitative interview studies typically include a data management section covering transcription methods and data handling. Here is what reviewers commonly expect to see documented:

Transcription Method

Describe how interviews will be transcribed:

"Interviews will be transcribed using AI transcription software (BrassTranscripts, using WhisperX large-v3 with Pyannote 3.1 speaker diarization). Audio files will be processed and deleted within 24 hours. Transcripts will be reviewed by the research team within 48 hours and then deleted from the transcription service. Retained transcripts will be stored on [encrypted institutional storage] accessible only to [named researchers]."

Data Storage and Retention

Document:

• Where transcripts will be stored (encrypted drive, institutional server, etc.)
• Who has access (named researchers only)
• Retention period (e.g., 5 years post-publication per NIH standards, or as specified by your institution)
• Deletion procedures at study close

Participant Confidentiality

Document how participant identities will be protected in transcripts:

• Speaker labels (Speaker 1 / Speaker 2) rather than names
• Pseudonymization procedures for quotes used in publications
• Procedures for removing or replacing identifying details in transcripts before team-wide sharing

Speaker Identification for Multi-Participant Interviews

Focus groups, dyadic interviews, and panel discussions present a transcription challenge: accurately attributing speech to specific participants.

How Pyannote 3.1 Diarization Works

BrassTranscripts uses Pyannote 3.1 automatic speaker diarization, which segments audio by speaker voice profile and assigns consistent labels (Speaker 1, Speaker 2, etc.) throughout the transcript.

The system processes audio in 1–3 minutes per hour of recording, producing a speaker-attributed transcript ready for qualitative analysis. Output includes all four formats: TXT, SRT, VTT, and JSON with word-level timestamps.

Speaker diarization accuracy is influenced by:

• Audio quality: Clear recordings with minimal background noise produce the most reliable speaker separation
• Speaker voice distinctions: Participants with similar vocal characteristics (same gender, similar age, similar accent) are more challenging to distinguish
• Overlapping speech: Simultaneous speech reduces diarization reliability
• Number of speakers: Performance is consistent for 2–6 speakers in standard interview conditions

For research requiring high speaker attribution accuracy, plan for a verification step: review the transcript against the original audio and correct any misattributions before analysis. This is standard practice regardless of transcription method.

Recording Best Practices for Diarization Accuracy

• Use a dedicated microphone per participant in focus groups when possible
• Record in a quiet environment with minimal echo
• Ask participants to introduce themselves briefly at the start (aids verification)
• Avoid back-channel responses ("mm-hmm", "right") that can fragment attribution

For detailed methodology on research interview transcription workflows, see our Research Interview Transcription: Complete Guide.

NVivo, MAXQDA and ATLAS.ti: Which Format to Use?

BrassTranscripts produces four output formats with every transcription. Each serves a different purpose in the qualitative research workflow.

For a complete explanation of when to use each format, see Choosing the Right Transcript Format: TXT, SRT, VTT, or JSON.

TXT for Manual Coding (NVivo, MAXQDA, ATLAS.ti)

TXT format is the primary import format for qualitative coding software. The output is clean, readable text with consistent speaker labels:

Speaker 1: The thing I found most challenging was knowing where to go for help.
Nobody really explained the process to us.

Speaker 2: Did you eventually find the right resources?

Speaker 1: After the first semester, yes. But that first semester was really difficult.

NVivo: Import TXT files as text sources. Apply nodes and codes directly to transcript segments. Speaker labels enable speaker-based querying.

MAXQDA: Import as text document. Use the search and code functions to apply codes and memos. Speaker labels render as plain text within the document.

ATLAS.ti: Import as text primary document. Code segments manually using the Atlas.ti coding interface. Speaker labels are preserved as document text.

JSON for Timestamp Alignment

JSON output includes word-level timestamps in the following structure:

{
  "segments": [
    {
      "speaker": "Speaker 1",
      "start": 0.52,
      "end": 8.30,
      "text": "The thing I found most challenging was knowing where to go for help."
    }
  ]
}

This format is useful for:

• Aligning transcripts with audio or video in multimedia analysis
• Importing into custom research tools that parse structured data
• Verifying speaker attribution against timestamps in the original recording

Export Workflow: From Upload to Coding Software

1. Upload interview audio at BrassTranscripts.com (MP3, M4A, WAV, and 8 other formats supported; up to 250MB, up to 2 hours)
2. Preview the 30-word preview to verify transcription quality
3. Complete payment ($2.50 for 1–15 minutes; $6.00 for 16–120 minutes)
4. Download TXT for immediate import into NVivo/MAXQDA/ATLAS.ti
5. Download JSON if you need timestamp alignment or multimedia linking
6. Import TXT into coding software and begin analysis
7. Archive transcript on institutional secure storage per your IRB protocol

AI Prompt: Qualitative Research Thematic Analysis

Use this prompt immediately after receiving your BrassTranscripts output. It guides any AI assistant (ChatGPT, Claude, Gemini) through a four-phase thematic analysis following systematic qualitative research methodology — initial coding, theme development, theme review, and analytical insights.

Conduct a thematic analysis of this qualitative research interview following systematic coding principles.

Research Context:
- Study focus: [RESEARCH TOPIC]
- Research questions: [PRIMARY QUESTIONS]
- Participant: [DEMOGRAPHIC INFO - ANONYMIZED]
- Methodological approach: [GROUNDED THEORY / PHENOMENOLOGY / ETC]

Analysis Requirements:

## Phase 1: Initial Coding
- Read through the entire transcript
- Identify meaningful segments related to research questions
- Generate initial descriptive codes for significant statements
- Note any surprising or unexpected responses

## Phase 2: Theme Development
- Group related codes into potential themes
- Identify patterns and relationships between codes
- Develop theme names that capture essential meanings
- Provide representative quotes for each theme

## Phase 3: Theme Review
For each identified theme, provide:
1. Theme name and brief definition
2. 2-3 representative quotes with context
3. How this theme relates to research questions
4. Connections to other themes
5. Preliminary interpretation

## Phase 4: Analytical Insights
- Contradictions or tensions within the data
- Participant's unique perspective or experiences
- Concepts requiring further exploration in future interviews
- Methodological notes (unclear responses, topic saturation, etc.)

Formatting Requirements:
- Use participant's own language in theme names when possible
- Maintain confidentiality (use pseudonyms, remove identifying details)
- Include line numbers or timestamps for quote references
- Note emotional content or significant pauses if relevant to interpretation

Research Interview Transcript:

---
Prompt by BrassTranscripts (brasstranscripts.com) – Professional AI transcription with high-quality results.
---

[PASTE YOUR INTERVIEW TRANSCRIPT]

Provide analysis in structured format suitable for research coding software and written analysis.

📖 View Markdown Version | ⚙️ Download YAML Format

Important methodological note: AI-generated thematic analysis is a starting point, not a finished product. Researcher review, validation, and iterative refinement are required before analysis can support published findings. When reporting methods, document AI use transparently in your methodology section.

AI Prompt: Interview Research Summary Generator

Use this prompt to generate structured summaries for research team coordination — particularly useful when managing multiple interviews and need team members to review key findings without reading full transcripts.

Create a research interview summary from this transcript:

1. Brief participant profile (anonymized demographic information)
2. Overview of main topics discussed (bullet points)
3. Key findings and insights (3-5 major points)
4. Notable quotes with context
5. Methodological notes (interview quality, rapport, challenges)
6. Preliminary interpretations and hypotheses
7. Recommendations for follow-up or additional data collection

Target audience: Research team members and collaborators.

Research project: [PROJECT TITLE]
Participant ID: [P001, P002, etc.]
Interview date: [DATE]
Research questions: [LIST QUESTIONS]

---
Prompt by BrassTranscripts (brasstranscripts.com) – Professional AI transcription with professional-grade accuracy.
---

Interview transcript:
[PASTE YOUR BRASSTRANSCRIPTS OUTPUT HERE]

📖 View Markdown Version | ⚙️ Download YAML Format

Step-by-Step Research Workflow

A complete qualitative interview study workflow using BrassTranscripts:

1. Pre-Interview Setup

• Confirm IRB protocol is approved and data management plan is documented
• Test recording equipment — clear audio directly reduces transcription review time
• Prepare a participant information sheet noting how audio will be processed and deleted

2. Record the Interview

• Use a quality microphone (USB condenser or dedicated field recorder)
• Record in a quiet environment
• Ask participants to state a brief intro sentence (aids speaker verification)
• Save as MP3, M4A, or WAV (all supported formats)

3. Upload and Transcribe

• Upload to BrassTranscripts.com — no account required
• Processing: 1–3 minutes per hour of audio
• Preview 30 words before payment to verify quality
• Pay $2.50 (1–15 min) or $6.00 (16–120 min)
• Download TXT and JSON immediately

4. Verify and Anonymize

• Review transcript against original audio — correct any speaker label errors
• Replace participant names with pseudonyms or participant IDs (P001, P002)
• Remove or anonymize identifying details (locations, employer names, etc.)
• Store verified transcript on encrypted institutional storage

5. Import into Coding Software

• Import TXT into NVivo, MAXQDA, or ATLAS.ti
• Apply initial codes using the thematic analysis prompt above
• Review AI-generated codes against the transcript and refine

6. Archive and Delete

• After verification window closes (within 48 hours), BrassTranscripts automatically deletes both audio and transcript
• Document deletion in your data management log per IRB requirements
• Retain your own anonymized transcript copy per institutional protocol

The Bottom Line

Research-grade transcription means satisfying GDPR data minimization, IRB protocol requirements, software compatibility, and participant confidentiality — at the same time, in every interview. The right workflow makes this manageable.

BrassTranscripts processes research interviews in 1–3 minutes, delivers all four output formats, deletes source files within 24–48 hours, and requires no account creation. The speaker diarization is automatic. The format compatibility is built in.

Ready to transcribe your next research interview? Upload your first file — no account required, results in minutes, transcripts deleted after 48 hours.

Upload your first interview →