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Trusted by Voice AI Teams Training Models at Scale

Annotated speech, conversation, and emotion data for teams building voice agents, IVR systems, and conversational AI, on the same platform and QC stack behind Taskmonk's other production AI data.
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Beyond Transcription: Data for Voice AI Agents That Read the Room

Your team already knows Taskmonk handles transcription and speaker diarization. Here's what it takes to train a voice agent that understands tone, context, and conversation flow.
Emotion & Sentiment Annotation
Emotional state, sentiment, stress level, tone, and confidence scoring, tagged at the utterance or turn level.
Conversation Annotation
Turn segmentation, dialogue flow, intent transitions, interruptions, escalation points, and resolution outcomes.
Audio Event Annotation
Background noise, music, silence, overlapping speech, laughter, and alarms, plus Taskmonk's filler-tag taxonomy
Need transcription and speaker diarization? You can find more details abt this on our Audio Annotation page.
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Get the Raw Audio Before You Even Get to Annotation

End-to-end speech data collection, from recruiting contributors to a validated, consented, quality-checked dataset, powered by Nimble, Taskmonk's own mobile companion app.
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Contributor onboarding and script distribution.
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Mobile and web recording via Nimble, Taskmonk's native Android and iOS app for audio, video, and image collection in the field.
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Consent Module: work is blocked until consent is accepted, gating every recording session by default.
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Demographic capture (where appropriate) and consent management.
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Automated audio quality checks before data enters the pipeline.

Voice AI Datasets That Reflect How People Actually Talk

Accent, dialect, speech rate, recording environment, and device type, captured so your model doesn't just work in a quiet studio.
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Speech rate and clarity, age group tagging.
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Noise level, recording environment, microphone/device type.
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Accent and language identification, dialect tagging, and speaker metadata including gender and nativity.
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Multilingual and code-switched speech support, with per-project multi-language configuration.

The Execution Layer for your Labeling-Ops

A unified execution layer that combines auto annotation, domain-qualified experts, and adaptive workflows to consistently deliver production-ready outcomes.
AI Pre-Labeling
Pre-labeling that runs on task-matched models:  SAM-3 for segmentation, YOLO v12 for detection, Mistral OCR for document extraction, Whisper-V3 and Google STT v2/Chirp for audio, with confidence-scored outputs routed to human review only where the model is uncertain. Swap models per task without re-architecting your pipeline.
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Workflow Orchestration
Task allocation runs on model/reviewer affinity, not a flat queue, each task goes to whichever model or specialist is calibrated for it. Maker-Checker and Consensus review sit on top as configurable QC layers, and every decision is versioned and traceable back to the model or reviewer that made it.
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Qualified Human Expertise
Specialists are qualified per task type before they touch your data: assessed, trained, certified, then monitored against live accuracy metrics, with a defined path to upskill into harder edge cases. Reviewers aren't generalists reassigned across projects; they're matched to the data type they were certified on.
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Voice AI, Wherever a Conversation Needs to Be Understood

The same conversation-and-emotion annotation stack adapts to the vertical.
Voice AI for Healthcare Operations · Voice-Enabled Physical AI & Robotics · AI Recruiters and Interviewers · Voice AI for Manufacturing · In-Car AI Assistants · Voice-First Operating Systems for Businesses
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CASE STUDY
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Indic Language Audio Annotation, 90 to 95% transcription accuracy across nine languages, up from a 50 to 60% baseline, for a Voice AI training-data provider

Ready to See It in Action?

The tooling behind this page, and what it takes to trust it with your data.
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Computer Vision
Bounding boxes, segmentation, and video annotation for the optical imagery underlying satellite and drone workflows.
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Physical AI
3D point-cloud annotation for autonomous driving, robotics, and infrastructure, beyond geospatial's 2D imagery.
Blogs

From the team running your labeling ops

Guides and playbooks from the Taskmonk blog for physical AI
Ecommerce Search Relevance: How to Fix It and Measure It
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Ecommerce Search Relevance: How to Fix It and Measure It
Taskmonk Marketing
September 7, 2026
Best Appen Alternatives for Enterprise Data Labeling in 2026
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Best Appen Alternatives for Enterprise Data Labeling in 2026
Taskmonk Marketing
August 25, 2026
AI Red Teaming: What It Is and How Labeled Data Powers It
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AI Red Teaming: What It Is and How Labeled Data Powers It
Taskmonk Marketing
August 25, 2026
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FAQ

How is this different from Taskmonk's Audio Annotation page?
Audio Annotation covers the core audio annotation tool and tooling, transcription, diarization, segmentation. Voice AI builds on top of that for teams training conversational agents, adding emotion, sentiment, and conversation-level annotation.
Can Taskmonk annotate emotion and sentiment in speech data?
Most projects use polygons and segmentation masks for buildings and land cover, polylines for roads and rivers, points for landmarks, and classification and attribute tagging to produce richer geospatial training data.
Does Taskmonk handle multi-turn conversation data, not just single audio clips?
Common inputs include GeoTIFF and Cloud Optimized GeoTIFF (COG), tiled imagery, multispectral imagery, LiDAR point clouds, and GIS vector layers. Outputs include GeoJSON, segmentation masks, vector polygons and polylines, plus metadata.
What if my audio isn't clean studio recordings?
We use clear labeling guidelines, edge-case playbooks, multi-pass QA, gold sets, and topology checks to prevent broken polygons, duplicate features across tiles, and class drift across regions and seasons.
Can Taskmonk help collect voice data, not just annotate what I already have?
Share a small sample dataset, label taxonomy, and target outputs. We return labeled examples with a QA snapshot and a scale plan, then move into production sprints with predictable deliveries.
Does this support multiple languages and accents?
Taskmonk's geo-tagging interface supports configurable RGB band mapping, so false-colour composites like NIR and SWIR display correctly for vegetation, moisture, and burn-scar work, and cumulative-cut contrast stretch, applied per band, to make low-contrast satellite imagery legible without leaving the tool, alongside native GPKG and GeoJSON export, and Google Maps, Esri, and OpenStreetMap basemaps with an Orthogonalise tool that snaps hand-drawn polygons to right angles.

Give Your Voice AI Something Real to Learn From

Emotion, conversation, and context-aware speech data, not just transcripts.
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