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Hotword customization is one of the concerned issues remained in ASR field -it is of value to enable users of ASR systems to customize names of entities,persons and other phrases to obtain better experience. The past few years haveseen effective modeling strategies for ASR contextualization developed, butthey still exhibit space for improvement about training stability and theinvisible activation process. In this paper we propose Semantic-AugmentedContextual-Paraformer (SeACo-Paraformer) a novel NAR based ASR system withflexible and effective hotword customization ability. It possesses theadvantages of AED-based model's accuracy, NAR model's efficiency, and explicitcustomization capacity of superior performance. Through extensive experimentswith 50,000 hours of industrial big data, our proposed model outperforms strongbaselines in customization. Besides, we explore an efficient way to filterlarge-scale incoming hotwords for further improvement. The industrial modelscompared, source codes and two hotword test sets are all open source.

SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability