Blog

Automatic transcription and language editing: what the machine captures reliably, where it systematically fails, and how the editor's role changes as they stop transcribing and start interpreting.

Transcription for journalism: how automation shortens the path from recording to quotation, what constitutes acceptable editing of direct speech, and where the limits of journalistic ethics lie.

Confidence score in transcription: what the 0–1 number actually measures, why a high value does not guarantee a correct transcript, and how to use the score effectively for prioritizing edits.

GDPR and transcription of sensitive recordings: cloud API as data transfer outside the EU, a pre-deployment checklist, and when local processing offers better protection than any contractual guarantee.

Medical transcription: why Latin-derived terminology, abbreviations, and eponyms cause general models to fail and how a terminology list and ensemble approach improve results.

Legal transcription: specifics of verbatim recording, requirements for court transcript format and archiving. Where automation saves transcribers' time and where human review must follow.

Real-time transcription or batch processing? Comparison across accuracy, latency, cost and implementation complexity. Practical guidance for choosing the right approach for your use case.

How chunking splits hour-long recordings for transcription APIs: silence detection, overlap, parallel processing and result merging. Why boundaries matter and how to spot errors.

Why combine multiple transcription models: the ensemble approach, ROVER algorithm, and how combining results reduces error rates.

How to prepare a recording for transcription: environment, microphone, format, test recording, and audio cleanup for better results.

How to turn a transcript into subtitles: BBC and Netflix readability standards, SRT/VTT synchronisation, editing, and language-specific considerations.

Automatic transcription in research: verbatim vs. clean transcription, ethics of cloud processing, citing transcripts in academic work.

Which audio formats and settings improve transcription accuracy: WAV, MP3, bitrate, sample rate, mono vs. stereo.

How algorithms predict punctuation in transcription, where they fail, and how to efficiently correct the result — a practical guide.

What WER measures, why 95% accuracy claims don't apply to your recording, and how to evaluate transcription quality in practice.

How to improve transcription of names, acronyms and domain terms: a practical guide to building and maintaining a custom terminology list.

How transcription differs for lectures, interviews, meetings, and podcasts: demands, typical problems, and recommendations for each recording type.

How speaker diarization recognizes who is speaking: the technical principle, conditions for reliability, and how to work with results.

Filler words in transcription: how they arise, when to remove them and when to keep them. A journalist's, researcher's, and corporate perspective.

Why automatic speech recognition struggles with Czech where it excels at English: morphology, diacritics, word order and training data. What actually helps.

How speech-to-text works under the hood: from audio digitization and spectral analysis to neural networks and their limits for non-English languages.