17MB vs 1.2GB: How a Tiny Model Beats Human Experts at Pronunciation Scoring

A technical deep-dive into building a pronunciation assessment engine that's 70x smaller than industry standard — and still outperforms human annotators. The Problem Pronunciation assessment is a $2.7B market growing at 18% CAGR, driven by 1.5 billion English learners worldwide. Yet the tools available today fall into two buckets: Cloud-only black boxes (Azure Speech, ELSA Speak) — accurate but expensive, opaque, and locked to specific vendors Academic models (wav2vec2 + GOPT) — open but massive (1.2GB+), requiring GPU inference and research-level expertise to deploy There's nothing in between. No lightweight, self-hostable engine that delivers expert-level accuracy. We built one. The Numbers We benchmarked against the speechocean762 dataset — the standard benchmark for pronunciation assessment, with 5,000 utterances scored by 5 expert annotators each. Metric Our Engine Human Experts GOPT (Academic) 3MH (SOTA) Phone-level PCC 0.580 0.555 0.679 — Word-level PCC 0.595 0.618 0.606 0.693 S