CONF Iuliia_TSD2025_2025/IDIAP Unifying Global and Near-Context Biasing in a Single Trie Pass. Iuliia, Thorbecke Villatoro-Tello, Esaú Zuluaga-Gomez, Juan Kumar, Shashi Burdisso, Sergio Rangappa, Pradeep Carofilis, Andrés Madikeri, Srikanth Motlicek, Petr S, Karthik Pandia D Hacioğlu, Kadri Stolcke, Andreas Aho-Corasick algorithm Contextualisation and adaptation of ASR real-time ASR transformer transducer EXTERNAL http://publications.idiap.ch/attachments/papers/2025/Iuliia_TSD2025_2025.pdf PUBLIC Text, Speech, and Dialogue. TSD 2025. Lecture Notes in Computer Science, Springer 16029 978-3-032-02547-0 2025 Springer https://link.springer.com/chapter/10.1007/978-3-032-02548-7_15 URL doi.org/10.1007/978-3-032-02548-7_15 doi Despite the success of end-to-end automatic speech recognition (ASR) models, challenges persist in recognizing rare, out-of-vocabulary words—including named entities (NE)—and in adapting to new domains using only text data. This work presents a practical approach to address these challenges through an unexplored combination of an NE bias list and a word-level n-gram language model (LM). This solution balances simplicity and effectiveness, improving entities’ recognition while maintaining or even enhancing overall ASR performance. We efficiently integrate this enriched biasing method into a transducer-based ASR system, enabling context adaptation with almost no computational overhead. We present our results on three datasets spanning four languages and compare them to state-of-the-art biasing strategies We demonstrate that the proposed combination of keyword biasing and n-gram LM improves entity recognition by up to 32% relative and reduces overall WER by up to a 12% relative.