Structured labels for sound and speech
Audio Annotation
Emotion tagging, acoustic event labeling, and speaker attribution built on top of high-accuracy transcription.
Overview
What is Audio Annotation?
Audio annotation enriches raw audio with emotion, intent, sentiment, and acoustic event labels — layered on top of transcription and diarization — to train voice AI and audio analytics models.
Why it matters: Voice assistants and audio analytics need more than words; they need to understand tone, emotion, and context. Enrichment labels turn raw speech into structured signals models can learn from.
Emotion Tagging
Multi-class emotion and sentiment labels aligned to speech segments
Acoustic Events
Non-speech sound classification for ambient and environmental audio
Speaker Attribution
Diarization with precise turn boundaries and speaker metadata
Intent Labels
Conversational intent and dialogue-act annotation
Workflow
How We Do It
01
Audio Assessment
We assess audio quality, content type, and annotation requirements to design the workflow.
02
Speaker Labeling
Diarization specialists identify and tag speaker turns with precise timestamps.
03
Enrichment
Emotion, sentiment, intent, and acoustic event labels added per your schema.
04
Accuracy Review
QA reviewers validate label consistency and acoustic event correctness.
05
Export
Delivered in JSON or custom format with word-level timestamps and QA metrics.
Case Study
Call Center AI Vendor
Call Center AI Vendor
Tag emotion and intent across 50,000 customer service calls
Solution
Layered enrichment pipeline on top of diarized transcripts with dual-reviewer QA
Results
50K
Calls annotated
95%
Label accuracy
6 wks
Delivery
Ready to Get Started with Audio Annotation?
Tell us about your project and we'll scope a pilot within 48 hours.