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What is Audio annotation?

Audio annotation means adding useful labels to sound files so AI models can understand them. People (and also models with human in the loop) mark what was said (transcription), who spoke (speaker diarization), where speech or events start and end (timestamps/segmentation), how something was said (sentiment, tone, emotion), and what else is audible (music, beeps, background noise).

Audio annotation teams may also tag language/dialect, call intent (e.g., “cancel order”), and hide/remove Personally Identifiable Information from audio or transcripts. Typical audio formats include WAV, MP3, OGG, and FLAC; many projects also handle audio that lives inside video files such as MP4 and WEBM.

In production, platforms like Taskmonk support end-to-end audio workflows—transcription and translation, speaker diarization, speech segmentation, and intent classification with multilingual and local-dialect coverage. Pre-labeling with trained models and keyboard shortcuts can speed up work, while vetted annotators handle edge cases. Quality is kept high with maker-checker review, editor passes, and majority vote.

Example

Scenario: You have a customer support team and want better self-service and QA.

  1. Collect 5,000 customer-agents calls.
  2. Annotators transcribe utterances with word-level timestamps, label speaker turns (Agent vs Customer), and tag intent (“refund request,” “address change”).
  3. They mark sentiment/emotion per turn, flag policy issues (promise made, escalation), and hide PII.

Export:

  • calls.jsonl with segments: {start, end, speaker, text, intent, sentiment, pii_redactions}
  • calls.rttm for diarization and calls.vtt for subtitles.

Why this helps:

  1. Train Automatic Speech Recognition(ASR) to lower Word Error Rate(WER) and power searchable call archives.
  2. Train intent and QA models to detect churn risk or compliance gaps.
  3. Give product and CX teams voice-of-customer insights by topic, tone, and outcome.

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