// transcription for researchers
In qualitative research, the transcript is not a convenience — it is the data. A misheard word changes a code. A dropped sentence removes context that shifts the meaning of a theme. When you quote a participant in a published paper, the words need to be exactly what they said.
SHRP is an AI transcription tool that produces near-verbatim transcripts with speaker labels, word-level confidence scores, and support for long recordings. It does not replace a human transcriptionist in every situation, but for many research workflows it gets you 90-95% of the way there — and you can verify the rest using the built-in confidence highlighting.
// verbatim accuracy
SHRP uses AssemblyAI, which delivers approximately 98% word accuracy on clean English audio. The transcription engine produces verbatim output — it transcribes what was said, including filler words, false starts, and self-corrections. It does not summarize or paraphrase.
Every word in the transcript carries a confidence score from 0 to 1. SHRP visualizes these scores with color-coded highlighting: words the model is unsure about appear in yellow or red. This means you do not need to re-listen to an entire 60-minute interview. You scan the confidence highlighting, spot the uncertain passages, and verify only those sections against the audio.
For thematic analysis, grounded theory, or discourse analysis, this level of accuracy is usually sufficient for initial coding. For conversation analysis or work where exact prosody and micro-pauses matter, you will still need human transcription — but SHRP can serve as a useful first pass to structure the recording before detailed manual work.
// speaker diarization
Speaker diarization automatically identifies and labels different speakers throughout the recording. In a standard semi-structured interview, SHRP labels the interviewer and participant separately. In a focus group with four or five participants, it detects each voice and assigns a consistent label.
The labels appear as Speaker A, Speaker B, etc. You can mentally map these to your participant codes (P01, P02) as you review. The diarization works best when speakers take turns — it can struggle with heavily overlapping speech, which is common in lively focus group discussions. In those cases, some utterances may be attributed to the wrong speaker and will need manual correction.
Speaker labels are preserved in all export formats, so they carry over when you move the transcript into your analysis software.
// long recordings
Research interviews are often 60 to 90 minutes, sometimes longer. SHRP handles these without issue. The Pro plan supports files up to 500MB, which covers several hours of compressed audio in MP3 or M4A format. WAV files are larger but also supported.
Processing time scales roughly linearly: a 60-minute recording takes about 5-8 minutes. A 90-minute recording takes about 8-12 minutes. You can close the browser tab and return later. The transcript is saved to your transcription history and will be waiting when you come back.
// privacy and ethics
If your research involves human participants, your IRB or ethics committee likely has requirements about how data is stored and processed. Here is what you need to know about SHRP.
Voice typing (microphone mode) runs entirely in your browser using the Web Speech API. Audio is processed locally and is never sent to SHRP servers. This mode is suitable for situations where data cannot leave your device.
File upload sends your audio to AssemblyAI for processing. AssemblyAI is SOC 2 Type II certified and GDPR compliant. Audio files are deleted from their servers after processing. SHRP does not retain copies of your uploaded audio beyond what is needed for transcription.
No training on your data. Neither SHRP nor AssemblyAI uses your audio or transcripts to train AI models. Your participants' words are not recycled into training datasets. Include this in your IRB data management plan as relevant.
// export for analysis
SHRP exports transcripts as plain text (.txt), which is the most universally compatible format for qualitative analysis software. The exported text includes speaker labels and can be imported directly into NVivo, Atlas.ti, Dedoose, MAXQDA, or any tool that accepts text files.
You can also copy the transcript to your clipboard and paste into a Word document for manual annotation, or into a spreadsheet if you prefer line-by-line coding. JSON export is available if you want programmatic access to word-level timestamps and confidence scores — useful for computational approaches to discourse analysis or if you are building a custom analysis pipeline.
SRT and VTT exports are also available. These are primarily for subtitling but can be useful if you need time-aligned transcript segments for media-synced coding in tools that support it.
// what it won't do
SHRP is an AI transcription tool, not a human transcription service. There are situations where it is not the right choice, and it is worth being upfront about those.
It does not capture non-verbal cues like laughter, sighs, or long pauses with precise duration markers. If your methodology requires these annotations, you need manual transcription.
Heavy accents or speakers using code-switching between languages may produce lower accuracy. The model works best with standard pronunciations in supported languages.
Medical, legal, or highly specialized terminology can be transcribed incorrectly. If your interviews are with clinicians discussing drug names or lawyers citing case law, expect to correct more words than usual.
Overlapping speech in group settings is the hardest problem in diarization. When two people talk at the same time, the model may drop words or misattribute them. For focus groups, plan to verify sections with heavy crosstalk.
It does not anonymize or de-identify participant data automatically. You are responsible for replacing names and identifying information before sharing transcripts.