L10N Estimator

STT (Speech-to-Text) Transcription

Comprehensive guide for AI-powered speech-to-text transcription services

Metrics

Breakdown:

  • STT: 1 minute per minute of runtime (60 runtime minutes/hour)
  • QA: 1 minute per minute of runtime (60 runtime minutes/hour)
  • Word Count: 150 words per runtime minute

Source Material

Required files and assets from the client:

  • Audio/video files: Audio or video files in common formats (.MP3, .WAV, .MP4, .MOV, etc.)
  • Audio quality: Clear audio with minimal background noise for best STT accuracy
  • Language specification: Source language of the audio content
  • Format requirements: Preferred output format (SRT, VTT, TXT, DOCX) and any specific formatting requirements
  • Timing requirements: Whether timestamps are needed and at what intervals

Best Practices

  • Ensure audio quality: Provide clear, high-quality audio files with minimal background noise for best STT accuracy
  • Use appropriate STT engine: Select STT engine optimized for the source language and audio quality
  • Specify language: Clearly specify the source language to ensure accurate transcription
  • Review and edit: STT transcriptions typically require review and editing for accuracy, especially for technical terms
  • Handle speaker identification: Some STT engines support speaker diarization; specify if speaker identification is needed
  • Format output: Format STT output according to requirements, adding timestamps and proper formatting
  • Verify technical terms: Review and correct technical terms, proper nouns, and specialized vocabulary
  • Maintain consistency: Ensure consistent formatting and terminology throughout the transcription
  • Handle unclear audio: Mark or correct unclear or inaudible sections in the transcription

Things to Consider

  • Audio quality: STT accuracy depends heavily on audio quality; poor audio may require additional editing time
  • Language support: STT engines vary in language support and accuracy; verify engine capabilities for target language
  • Speaker accents: Heavy accents or non-native speakers may reduce STT accuracy and require more editing
  • Technical terminology: STT may struggle with technical terms, proper nouns, and specialized vocabulary; review is essential
  • Background noise: Background music, noise, or overlapping speech can significantly impact STT accuracy
  • Multiple speakers: STT speaker diarization may not be as accurate as human transcription for multiple speakers
  • Editing requirements: STT transcriptions typically require editing and review for accuracy and formatting
  • Turnaround time: STT transcription is faster than human transcription but may require more editing time
  • Cost vs. accuracy: STT is cost-effective but may require additional review time for high-accuracy requirements

Workflow

  1. File Receipt: Receive audio or video files and verify quality and format
  2. STT Processing: Process audio through STT engine, generating initial transcription (2 minutes per runtime minute)
  3. Initial Review: Review STT output for accuracy, identifying errors and areas requiring correction
  4. Editing and Correction: Edit and correct STT transcription, fixing errors, technical terms, and proper nouns
  5. Speaker Identification: If required, add or correct speaker identification throughout the transcription
  6. Timestamp Addition: Add or verify timestamps at specified intervals if required for video synchronization
  7. Terminology Review: Verify and correct technical terms, proper nouns, and specialized vocabulary
  8. Formatting: Apply required formatting, including paragraph breaks, punctuation, and style guidelines
  9. Quality Review: Review transcription for accuracy, completeness, and formatting consistency
  10. Final Output: Deliver transcription in requested format (SRT, VTT, TXT, DOCX) with timestamps if required