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The success of speech assistants requires precise recognition of a number ofentities on particular contexts. A common solution is to train a class-basedn-gram language model and then expand the classes into specific words orphrases. However, when the class has a huge list, e.g., more than 20 millionsongs, a fully expansion will cause memory explosion. Worse still, the listitems in the class need to be updated frequently, which requires a dynamicmodel updating technique. In this work, we propose to train pruned languagemodels for the word classes to replace the slots in the root n-gram. We furtherpropose to use a novel technique, named Difference Language Model (DLM), tocorrect the bias from the pruned language models. Once the decoding graph isbuilt, we only need to recalculate the DLM when the entities in word classesare updated. Results show that the proposed method consistently andsignificantly outperforms the conventional approaches on all datasets, esp. forlarge lists, which the conventional approaches cannot handle.

Phrase-Level Class based Language Model for Mandarin Smart Speaker Query Recognition