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BY COPPER SUN
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•8 min read•BrassTranscripts Team

Research Interview Transcription Guide [2025]

Interview TranscriptionTranscription Accuracy

AI transcription processes research interviews in 1-3 minutes per hour with automatic speaker identification—here's how to get research-ready transcripts that meet qualitative research standards.

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Why Transcription Quality Matters for Research

Qualitative research depends on accurate representation of participant voices. According to the Qualitative Research Guidelines Project[1], transcription decisions affect:

  • Data integrity: Inaccurate transcription introduces systematic errors
  • Analysis validity: Themes emerge from actual participant language
  • Audit trails: Reviewers need verifiable source material
  • Ethical representation: Participants' words should be faithfully recorded

Traditional manual transcription takes 4-6 hours per hour of audio[2]. AI transcription reduces this to 1-3 minutes while maintaining consistent quality across all interviews—no transcriber fatigue or inconsistency between sessions.

[1] Robert Wood Johnson Foundation Qualitative Research Guidelines Project [2] Industry standard documented by Rev.com and transcription service providers

Verbatim vs. Clean Transcription

Verbatim Transcription

Captures everything exactly as spoken:

  • Filler words (um, uh, like, you know)
  • False starts and self-corrections
  • Overlapping speech
  • Non-verbal sounds (laughter, sighs, pauses)

Best for: Discourse analysis, conversation analysis, linguistic research

Clean (Intelligent) Transcription

Removes verbal clutter while preserving meaning:

  • Filler words removed
  • Grammar lightly corrected
  • False starts cleaned up
  • Content and meaning preserved

Best for: Thematic analysis, content analysis, applied research

What AI Transcription Produces

BrassTranscripts produces clean verbatim output by default:

  • Filler words included (um, uh)
  • Speaker labels preserved
  • False starts captured
  • Natural speech patterns maintained

For strict verbatim notation (overlaps, precise pause lengths), researchers typically add markup during the verification stage.

Speaker Identification for Multi-Participant Interviews

How Automatic Speaker Diarization Works

BrassTranscripts uses Pyannote 3.1 for speaker identification:

  1. Voice detection: System identifies distinct voice patterns
  2. Segmentation: Audio divided by speaker changes
  3. Labeling: Consistent labels applied (Speaker 1, Speaker 2, etc.)
  4. Output: Transcript formatted with speaker turns

Sample Output

Speaker 1: Can you tell me about your experience with the program?

Speaker 2: Sure. I started in January, and at first I was skeptical. But after the first month, I noticed real changes in how I approached the work.

Speaker 1: What kind of changes specifically?

Speaker 2: Mostly in my confidence level. I used to second-guess every decision.

Mapping Speaker Labels to Participant IDs

After transcription, create a speaker key:

Label Participant ID Role
Speaker 1 R01 Researcher/Interviewer
Speaker 2 P01 Participant

Use find-and-replace to anonymize transcripts for analysis:

  • Speaker 1 → Interviewer
  • Speaker 2 → Participant_01

Focus Groups and Multi-Speaker Settings

For focus groups with 3+ participants:

  • Speaker diarization accuracy depends on voice distinctiveness
  • Very similar voices may occasionally be merged
  • Recommend seating participants at consistent distances from microphone
  • Consider individual lapel mics for critical research

Research Ethics and Data Privacy

IRB and Ethics Considerations

When using any transcription service, researchers must consider:

  1. Data handling: Where is audio stored? For how long?
  2. Third-party access: Who processes the data?
  3. Encryption: Is data protected in transit and at rest?
  4. Retention: When is data deleted?

BrassTranscripts Data Practices

Concern BrassTranscripts Approach
Audio storage Deleted after 24 hours
Transcript storage Available for 48 hours, then deleted
Account data No account required—no personal data stored
Encryption Files encrypted during upload and storage
Location Processing on secure cloud infrastructure

Documenting Transcription Method

For your methods section, document:

Audio recordings were transcribed using BrassTranscripts
(brasstranscripts.com), an AI transcription service using
WhisperX large-v3 with Pyannote 3.1 speaker diarization.
Transcripts were verified against original recordings by [researcher].
Audio files were automatically deleted by the service after 24 hours.

Output Formats for Analysis Software

TXT Format (Plain Text)

Speaker 1: How would you describe your overall experience?
Speaker 2: I would say it was transformative. Really changed how I think about the work.

Best for: Manual coding, importing into any software

JSON Format (Structured Data)

{
  "segments": [
    {
      "speaker": "Speaker 1",
      "start": 0.0,
      "end": 3.2,
      "text": "How would you describe your overall experience?"
    },
    {
      "speaker": "Speaker 2",
      "start": 3.5,
      "end": 8.1,
      "text": "I would say it was transformative. Really changed how I think about the work."
    }
  ]
}

Best for:

  • NVivo: Import as text, use timestamps for media sync
  • Atlas.ti: JSON import for structured coding
  • Custom analysis: Programmatic processing with Python/R

SRT/VTT Formats

1
00:00:00,000 --> 00:00:03,200
[Speaker 1] How would you describe your overall experience?

2
00:00:03,500 --> 00:00:08,100
[Speaker 2] I would say it was transformative. Really changed how I think about the work.

Best for: Video analysis, multimedia research, accessibility

Step-by-Step Research Workflow

Step 1: Prepare Your Recording

  • Use a quality microphone (USB condenser recommended)
  • Record in a quiet space with minimal background noise
  • Position mic 6-12 inches from speakers
  • Test audio levels before the interview begins

Step 2: Upload and Transcribe

  1. Go to BrassTranscripts
  2. Upload your audio file (supports MP3, M4A, WAV, MP4, and more)
  3. Wait 1-3 minutes per hour of audio
  4. Download all four formats (TXT, SRT, VTT, JSON)

Step 3: Verify Critical Sections

Researchers should verify transcripts against original audio for:

  • Direct quotes used in publications
  • Ambiguous passages
  • Sections with overlapping speech
  • Technical terminology or proper nouns

Time-saving tip: Use the JSON timestamps to jump directly to specific sections in your audio.

Step 4: Anonymize for Analysis

Before importing to analysis software:

  • Replace speaker labels with participant codes
  • Remove identifying information
  • Apply your IRB-approved anonymization protocol

Step 5: Import to Analysis Software

NVivo:

  1. Import TXT as internal source
  2. Auto-code by speaker using paragraph styles
  3. Link to media file using timestamps from JSON

Atlas.ti:

  1. Import TXT as primary document
  2. Use JSON for timestamp synchronization
  3. Apply speaker-based coding

Dedoose:

  1. Upload TXT transcript
  2. Add descriptor fields from speaker key
  3. Begin coding process

Audio Quality Best Practices

Recording Setup Checklist

  • Quiet room with minimal echo
  • Quality microphone (not laptop built-in)
  • Microphone positioned correctly
  • Test recording before interview
  • Backup recording device if possible

Common Issues and Solutions

Issue Impact on Transcription Solution
Background noise Reduced accuracy Record in quiet space
Echo/reverb Speaker detection errors Use soft furnishings, closer mic
Distant microphone Quiet audio, missed words Position mic 6-12 inches from speakers
Overlapping speech Merged speaker segments Brief pauses between speakers
Phone/video call quality Variable accuracy Use highest quality settings

For Remote Interviews

  • Use Zoom's "Original Sound" setting for better audio
  • Ask participants to use headphones (reduces echo)
  • Record locally when possible (better quality than cloud recording)
  • Have backup recording on participant's end if critical

FAQ

How long does AI transcription take for research interviews?

AI transcription processes audio at 1-3 minutes per hour of recording. A 60-minute interview typically completes in 1-3 minutes.

Does AI transcription include speaker identification?

Yes. BrassTranscripts uses Pyannote 3.1 for automatic speaker diarization, labeling each speaker consistently throughout the transcript (Speaker 1, Speaker 2, etc.).

Is AI transcription accurate enough for qualitative research?

For clear audio with minimal background noise, AI transcription provides professional-grade accuracy. Researchers should verify critical quotes against the original recording, as with any transcription method.

How is research data privacy handled?

BrassTranscripts automatically deletes audio files after 24 hours and transcripts after 48 hours. No account creation required, and no data is stored long-term.

What output formats work with qualitative analysis software?

JSON format includes word-level timestamps suitable for NVivo and Atlas.ti import. TXT format works for manual coding. SRT/VTT formats support multimedia analysis.

Can I cite AI transcription in my methods section?

Yes. Document the service and technology used (WhisperX large-v3, Pyannote 3.1 speaker diarization), your verification process, and data handling practices.

What about non-English interviews?

BrassTranscripts supports 99+ languages with automatic language detection. For multilingual interviews, the system detects the primary language. Accuracy varies by language—major languages like Spanish, French, German, and Mandarin have strong support.

How do I handle sensitive research data?

The 24-hour audio deletion and 48-hour transcript deletion meet many IRB requirements. Download your files promptly, store them according to your approved protocol, and document the transcription service's data practices in your IRB application.

Ready to transcribe your research interviews? Upload your recording and get speaker-labeled transcripts in minutes. Processing takes 1-3 minutes per hour of audio, with automatic deletion for research data privacy.

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