Emotion tags, acoustic events and speaker attribution.
Overview
What is audio annotation?
Audio annotation enriches raw audio with emotion, intent, sentiment and acoustic event labels, layered on transcription and diarization, to train voice AI and audio analytics.
Voice assistants need more than words. They need tone, emotion and context. Enrichment labels turn raw speech into structured signals models can learn from.
Multi-class emotion and sentiment labels aligned to speech segments.
Non-speech sound classification for ambient and environmental audio.
Diarization with precise turn boundaries and speaker metadata.
Conversational intent and dialogue-act annotation.
Workflow
How we do it
- 01Audio assessment
We assess audio quality, content type and annotation requirements to design the workflow.
- 02Speaker labeling
Diarization specialists identify and tag speaker turns with precise timestamps.
- 03Enrichment
Emotion, sentiment, intent and acoustic event labels added per your schema.
- 04Accuracy review
QA reviewers validate label consistency and acoustic event correctness.
- 05Export
Delivered in JSON or custom format with word-level timestamps and QA metrics.
Case study
Tag emotion and intent across 50,000 customer service calls
Layered enrichment pipeline on diarized transcripts, with dual-reviewer QA.
Ready to get startedwith audio annotation?
Tell us about your project and we will scope a pilot within 48 hours.