Evaluate Speech-to-Text Quality for a Contact-Center Analytics Vendor
Overview
What this challenge is about.
You calculate WER, domain-term recall, and entity F1 across three engines on 200 call snippets, then recommend per language. Get a verifiable certificate.
The Brief
What you'll do, and what you'll demonstrate.
Pick the best speech-to-text engine (or mix) for a multilingual contact-center analytics product on cost-adjusted accuracy.
Earning criteria — what you'll demonstrate
- Apply standard speech-to-text evaluation metrics across multiple languages
- Quantify domain-term recall and named-entity accuracy alongside WER
- Combine accuracy and cost into a procurement-grade recommendation
- Communicate licensing-relevant findings to a procurement-aware stakeholder
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career paths this builds toward
Canonical rolesApplied AI Scientist
Multilingual model bake-offs with cost-adjusted recommendations are the applied-AI-scientist's bread-and-butter at B2B AI vendors.
This challenge sharpens
- speech-recognition
- benchmarking
- multilingual-evaluation
NLP Engineer
Hands-on evaluation across multiple speech engines is core NLP-engineer territory; the named-entity sub-task bridges directly into NER work.
This challenge sharpens
- speech-recognition
- model-evaluation
- multilingual-evaluation
ML Researcher
Designing fair multi-engine benchmarks with confidence intervals is the kind of rigor ML researchers in industry are graded on.
This challenge sharpens
- benchmarking
- sequence-models
- model-evaluation