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 scenario
The vendor (around 130 staff, EUR 22M ARR) is renegotiating speech-to-text licensing this quarter and a wrong pick would cost the platform an estimated EUR 700K over the contract term.
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.
Machine Perception
Master · Computer Vision
Strong alignment
This challenge maps to Machine Perception at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Speech Recognition
Apply speech recognition to solve real industry problems and demonstrate production-level capability.
- Sequence Models
Apply sequence models to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
- Multilingual Evaluation
Apply multilingual evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Applied 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