Hotword customization is crucial in ASR to enhance the accuracy of domain-specific terms. It has been primarily driven by the advancements in traditional models and Audio large language models (LLMs). However, existing models often struggle with large-scale hotwords, as the recognition rate drops dramatically with the number of hotwords increasing. In this paper, we introduce a novel hotword customization system that utilizes a hotword pre-retrieval module (H-PRM) to identify the most relevant hotword candidate by measuring the acoustic similarity between the hotwords and the speech segment. This plug-and-play solution can be easily integrated into traditional models such as SeACo-Paraformer, significantly enhancing hotwords post-recall rate (PRR). Additionally, we incorporate H-PRM into Audio LLMs through a prompt-based approach, enabling seamless customization of hotwords. Extensive testing validates that H-PRM can outperform existing methods, showing a new direction for hotword customization in ASR.
@article{arxiv.2508.18295,
title = {H-PRM: A Pluggable Hotword Pre-Retrieval Module for Various Speech Recognition Systems},
author = {Huangyu Dai and Lingtao Mao and Ben Chen and Zihan Wang and Zihan Liang and Ying Han and Chenyi Lei and Han Li},
journal= {arXiv preprint arXiv:2508.18295},
year = {2025}
}