Blog

Academic transcription from dictated notes to focus groups: verbatim conventions, Jefferson Notation, CHAT format, import into MAXQDA/NVivo, and GDPR in research.

Dialect, accent, and non-standard speech in automatic transcription: why models prefer standard language, where they fail, and what ensemble or custom glossaries can realistically change.

Podcast transcription: from recording through diarization to show notes, transcript pages, and subtitles. Where automation saves hours and what editing for derived materials remains.

Web interface or API for transcription: comparison across ease of use, scalability, cost, and flexibility. Five questions and a rule of thumb for deciding.

How ASR models work: mel spectrograms, CTC, transformer architecture, training data, and why some languages are harder than others for automatic transcription.

How to transcribe meetings and conference calls: speaker diarization, recording equipment, output formats, and data protection rules for recording meetings.

When to include timestamps in a transcript and when to leave them out. A guide to SRT, VTT, and JSON formats for subtitles, research, legal records, and quick notes.

How live transcription works in the browser: WebSocket, MediaRecorder API, and transcription models — a technical explanation without requiring expertise.

How to compare transcription services without marketing hype: how to prepare a mini-test, which criteria to track, and how to avoid common traps.

How automatic transcription helps in education: lecture subtitles, accessibility requirements, study notes, and the workflow from recording to SRT and VTT export.

How noise, echo, and compression increase transcription error rates: what SNR, RT60, and WER are and how to improve recording quality before transcription.

Call centre transcription requires knowledge of 8 kHz phone audio quality, MiFID II and GDPR requirements, and post-processing capabilities.

Transcribing historical archival recordings: how to pre-process tape recordings, what SNR is needed for automation, and how to follow TEI-XML archival standards.

Local Whisper vs. cloud transcription: comparison of accuracy, speed, GDPR compliance, and cost. When data stays on your server and when cloud is the right choice.

How to build an automated transcription pipeline from recording to document: triggers, webhooks, n8n, Python, post-processing, and quality monitoring.

A practical overview of copyright law for transcriptions: when transcription is legal, when you need consent, and what to watch out for when storing and sharing.

An overview of trends in speech transcription: end-to-end models, foundation models like Whisper and Google USM, real-time latency, and the unsolved problems of today's technology.

How LLMs process speech transcripts: meeting summaries, Q&A extraction, podcast chapters, and the risks of hallucination. A guide to the LLM pipeline for transcription.

How to transcribe recordings with multiple languages: code-switching, language detection in Whisper, and practical tips for mixed-language content.

How to create subtitles for Instagram, TikTok, and YouTube from automatic transcription: SRT export, platform requirements, and tips for quick correction.

Why speech transcription does not capture emotion and vocal tone, how sentiment analysis and Speech Emotion Recognition work, and what their limits are.

How automatic transcription accelerates customer research, which tools (Atlas.ti, NVivo, Dovetail) integrate it, and what researchers need to know about accuracy and diarization.

Legal obligations for transcription and captioning of recordings in public administration: accessibility legislation, WCAG 2.1, archiving, and data protection.

How speech transcription speeds up content creation — a practical workflow for podcasts, interviews, webinars, and YouTube videos.

How to use text transcripts with videos and podcasts for better search engine discoverability: what crawlers actually see, where to place transcripts, and how to add structured data.

An overview of the most frequent and most insidious automatic transcription errors and practical review procedures for catching and fixing them in time.

How to use voice dictation and automatic transcription for personal productivity: from morning routines to capturing ideas and integration with Obsidian and Notion.

How to calculate whether you can transcribe 50 hours of recordings over a weekend: what determines speed, where limits arise, and how to plan parallel processing and result review.

A checklist for ordering transcription externally: recording preparation, requirement specification, data protection, and quick review of the delivered result.

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

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

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

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

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

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

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

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

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

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

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

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.

WER requires a reference text that cannot be produced for every recording in production. Confidence score, inter-model agreement, and sampling are practical alternatives for monitoring transcription quality without manual annotation.

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

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

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.

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.

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

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

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.

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.

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.

TXT, SRT, VTT, JSON, CSV — what each transcription format contains, what it is suitable for, and why missing data from a richer format cannot be reconstructed afterwards.