Transkribeeri

Transkribeeri is a term used primarily in Estonian-language contexts to describe the practice or system of turning spoken language into written text. It can denote the act of transcription as well as software, services, or workflows that perform transcription. Transkribeeri encompasses both orthographic transcription, which preserves standard spelling, and phonetic transcription, such as IPA.

In practice, transkribeeri workflows may be automated, manual, or a mix. Automated transcription uses algorithms for speech recognition and typically produces time-stamped transcripts with speaker labels and confidence scores. Manual transcription involves human transcribers, often with specialized annotation for punctuation, disfluencies, and non-speech events. Output formats commonly include plain text, as well as subtitle formats like SRT or VTT.

Technology: Modern transkribeeri systems rely on machine learning and neural networks, often leveraging pre-trained models and language-specific resources. They may include features like diarization, noise reduction, and punctuation restoration. Some systems support multiple languages and dialects, as well as phonetic transcription in addition to orthographic text.

Applications: Transkribeeri is used in media captioning, journalism, academic research, archival documentation, legal proceedings, and accessibility services. It is also an area of study in computational linguistics, focusing on accuracy, ethics, and data privacy.

Standards and challenges: Accuracy varies with audio quality, speaker overlap, and language. Efforts to standardize metadata, timestamps, and speaker labeling continue, with quality-control practices that combine automated scoring and human review.

See also: transcription, speech recognition, phonetic transcription.