Building a reliable voice NLU system depends on the quality, coverage, and control of your test data. Cekura provides purpose-built test data management tools designed for teams validating speech understanding in real-world conditions, across accents, environments, and conversation styles.
This goes beyond simply storing audio. It focuses on intentionally building, expanding, and validating the data your voice NLU relies on to perform in real conversations.
Broad, Realistic Voice Coverage by Design
Cekura helps teams curate test datasets that reflect how people actually speak.
You can organize and test utterances across:
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Regional and non-native accents
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Different speaking speeds, pauses, and interruptions
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Emotional tones like frustration, urgency, or hesitation
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Noisy environments including background chatter or low audio quality
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Domain-specific vocabulary such as product names, medical terms, or account identifiers
Utterances can be tagged by scenario, persona, accent, noise condition, and intent, making it easy to see what your NLU has truly been tested against and what is missing.
Flexible Data Ingestion From Real Systems
Cekura makes it easy to bring in voice data from where it already lives.
Teams can ingest:
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Audio from calls and simulations
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Transcripts alongside recordings
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Metadata such as timestamps, speaker roles, and call context
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Logs from IVR systems, voice apps, and production environments via API
This allows test datasets to evolve naturally from real user interactions instead of relying on static, hand-written examples.
Automated Test Data Generation at Scale
To expand coverage quickly, Cekura can generate new test data automatically.
Teams use it to:
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Create synthetic utterances from prompts or knowledge bases
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Generate multiple variations of the same request to test robustness
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Produce edge cases that rarely appear in production
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Simulate difficult conditions like interruptions, silence, or conflicting inputs
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Run repeated variations to capture non-deterministic behavior
This ensures your NLU is tested not just on happy paths, but on the scenarios most likely to fail in the real world.
Review, Labeling, and Ground Truth Control
Cekura includes workflows to review and validate test data before it is trusted.
Teams can:
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Inspect audio and transcripts side by side
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Apply multiple labels per utterance including intent, entities, and context
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Track annotation history and changes over time
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Build consensus across reviewers when needed
This keeps test data consistent, auditable, and aligned with how success is defined for your voice NLU.
Built-In Data Quality Checks
Cekura actively helps maintain clean and reliable datasets.
It identifies:
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Duplicate or near-duplicate utterances
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Low-quality or corrupted audio
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Transcript mismatches or incomplete data
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Speech quality issues that affect recognition accuracy
Automatic metrics like speech clarity, noise levels, and interruption timing help teams spot data issues before they affect NLU performance.
Coverage Insights and Gap Detection
Understanding what your test data covers is as important as having the data itself.
Cekura provides visibility into:
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Which intents and entities are over or underrepresented
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Scenario distribution across personas and environments
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Missing combinations that have never been tested
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How coverage changes as new data is added
This makes test planning a continuous, data-driven process instead of guesswork.
Privacy-First Handling of Voice Data
Voice data often contains sensitive information. Cekura is built to handle it responsibly.
The platform supports:
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Encryption in transit and at rest
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Role-based access controls and audit logs
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Automatic redaction of sensitive information from audio and transcripts
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Compliance-ready workflows for regulated environments
This allows teams to use real customer data for testing without compromising privacy.
Designed for Automation and Scale
Cekura’s test data management tools are built to integrate into modern development workflows.
Teams can:
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Trigger tests programmatically via API
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Reuse the same datasets across model versions
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Run regression tests automatically as data grows
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Scale to thousands of audio samples without manual overhead
As voice systems evolve, your test data stays structured, traceable, and ready to validate what changes actually matter.
The Result
Cekura gives voice NLU teams control over their test data lifecycle. From ingestion and generation to labeling, quality checks, and coverage analysis, it ensures your NLU is trained and validated against the voices, conditions, and edge cases it will face in production.