Selected Delivery Experience
Low-Resource Language Models
Improved speech and intent recognition for language markets where conventional training data and model coverage were limited.
Context
Customer interactions in targeted language markets could not be served reliably by default speech and intent systems because representative data and language resources were scarce.
Why the problem was difficult
Low-resource languages expose compound constraints: limited labeled data, dialect variation, code-switching, domain terminology, and uneven baseline model performance.
Approach
- Focused evaluation on usable coverage in the target operating context
- Improved speech and intent-recognition performance through targeted adaptation
- Designed quality review around market-specific error patterns
- Connected model improvement to practical call-analysis coverage
Production challenges addressed
Representative evaluation dataMarket-specific error analysisQuality thresholds for usable coverageOperational monitoring across language variation
Measured results
Only verified scale and outcomes are shown.
- ~30–50% lower speech and intent-recognition error
- More than 80% usable call coverage in targeted markets
Key lesson
Model quality is contextual. Practical coverage improves when evaluation, adaptation, and operating thresholds reflect the language and workflow actually being served.
A useful next step