Beyond WER: Nuanced Evaluation Metrics for African Speech Model
Speech models can achieve impressive benchmark scores and still fail real users.
In African production environments, speech systems must handle accents, multilingual speakers, code-switching, noisy call-center audio, unstable networks, and varying levels of language fluency - challenges that traditional metrics like Word Error Rate (WER) often fail to capture.
This session will be led by Bunmi Akinremi, Technical Lead at PyData Lagos, where she works on advancing practical machine learning and AI systems through community, education, and applied technical initiatives.
Together, we’ll explore what it really means to evaluate speech systems beyond benchmark performance - from semantic correctness and user experience to reliability in real-world African deployment environments.
We’ll cover:
The Reality of African Speech Models in Production
Why Traditional Metrics Break Down
Beyond WER: Production-Grade Evaluation
Monitoring Speech Models in Production
Whether you’re building speech AI, working with voice products, or curious about production ML systems, this session will give you a more grounded perspective on what “good” speech models actually look like in practice.
📅 Date: Thursday, May 28th, 2026
⏰ 6:00 PM (WAT)
📍 Register to attend
We look forward to having you! 🚀