Hotword customization is one of the important issues remained in ASR field - it is of value to enable users of ASR systems to customize names of entities, persons and other phrases. The past few years have seen both implicit and explicit modeling strategies for ASR contextualization developed. While these approaches have performed adequately, they still exhibit certain shortcomings, such as instability in effectiveness, especially in non-autoregressive ASR models. 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 combines the accuracy of the AED-based model, the efficiency of the NAR model, and the excellent performance in contextualization. In tens of thousands of hours industrial big data experiments, our proposed model outperforms strong baselines in customization and general ASR tasks. Besides, we explore an efficient way to filter large scale incoming hotwords for further improvement.

我们提出了一种具有灵活和有效的热词自定义能力的新型非自回归型ASR系统，该系统结合了基于AED模型的准确性、非自回归模型的高效性以及上下文化的出色性能，在大规模实验中优于其他基线模型，同时我们还探索了一种高效的方式来过滤大规模的热词以进一步提高性能。

SeACo-Paraformer: 一个灵活且高效的非自回归ASR系统，具备可定制化的热词能力