Speech-to-Text / ASR

Also known as: ASR, Automatic Speech Recognition, Auto-transcription, Whisper

Technology that transcribes audio from interviews, focus groups, or calls into text to facilitate AI analysis.

Speech-to-Text (STT) or Automatic Speech Recognition (ASR) is technology that converts spoken audio into written text. In qualitative research, its primary application is automatic transcription of in-depth interviews, focus groups, call center recordings, and co-creation sessions.

Leading models such as Whisper (OpenAI), Deepgram, and AssemblyAI achieve precision levels above 95% in English and increasingly better in Spanish, including Latin American variants.

Automatic transcription reduces interview transcription time from hours to minutes, and allows the resulting text to be immediately analyzed by NLP models for coding, sentiment analysis, and topic modeling.

An important consideration: ASR systems have higher error rates with strong regional accents, multiple simultaneous speakers (diarization), or poor audio conditions. Human review of critical transcriptions is always recommended.

Atlantia uses STT in its qualitative projects to accelerate the analysis of recorded sessions.