  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 have
seen effective modeling strategies for ASR contextualization developed, but
they still exhibit space for improvement about training stability and the
invisible activation process. In this paper we propose Semantic-Augmented
Contextual-Paraformer (SeACo-Paraformer) a novel NAR based ASR system with
flexible and effective hotword customization ability. It possesses the
advantages of AED-based model's accuracy, NAR model's efficiency, and explicit
customization capacity of superior performance. Through extensive experiments
with 50,000 hours of industrial big data, our proposed model outperforms strong
baselines in customization. Besides, we explore an efficient way to filter
large-scale incoming hotwords for further improvement. The industrial models
compared, source codes and two hotword test sets are all open source.
