Space-Efficient Representation of Entity-centric Query Language Models
Christophe Van Gysel, Mirko Hannemann, Ernest Pusateri, Youssef Oualil, Ilya Oparin · Interspeech 2022 · 2022
Virtual assistants make use of automatic speech recognition (ASR) to help users answer entity-centric queries.However, spoken entity recognition is a difficult problem, due to the large number of frequently-changing named entities.In addition, resources available for recognition are constrained when ASR is performed on-device.In this work, we investigate the use of probabilistic grammars as language models within the finite-state transducer (FST) framework.We introduce a deterministic approximation to probabilistic grammars that avoids the explicit expansion of non-terminals at model creation time, integrates directly with the FST framework, and is complementary to n-gram models.We obtain a 10% relative word error rate improvement on long tail entity queries compared to when a similarly-sized n-gram model is used without our method.