GeoPOI: Scalable Post-Training for Selective Geographic Contextualization in ASR

Recognizing point-of-interest (POI) names requires geographic knowledge, but irrelevant context can introduce transcription errors. We present GeoPOI, a framework for selective geographic contextualization with a frozen ASR backbone. Cell-specific adapters learn regional knowledge through difficulty-prioritized, geographically sharded post-training. A staged pipeline uses predicted entity spans for acoustic retrieval, teacher-anchored ranking to select relevant names, and trie-based logit biasing to guide transcription. Audio-conditioned routing selects a single cell, a GPS neighborhood, or the unmodified backbone. Training draws on over ten million utterances covering approximately four million POIs. On the evaluation set, with geographic gating enabled, POI character error rate (CER) falls from 14.07% to 5.24% (63% relative reduction). Component comparisons also show a 29% relative reduction in overall CER and increased all-target name containment from 47.8% to 83.0%, with general-speech CER essentially unchanged (4.92% versus 4.86%). We will publicly release our code, trained model weights, and evaluation dataset.