  Open-vocabulary keyword spotting (KWS), which allows users to customize
keywords, has attracted increasingly more interest. However, existing methods
based on acoustic models and post-processing train the acoustic model with ASR
training criteria to model all phonemes, making the acoustic model
under-optimized for the KWS task. To solve this problem, we propose a novel
unified two-pass open-vocabulary KWS (U2-KWS) framework inspired by the
two-pass ASR model U2. Specifically, we employ the CTC branch as the first
stage model to detect potential keyword candidates and the decoder branch as
the second stage model to validate candidates. In order to enhance any
customized keywords, we redesign the U2 training procedure for U2-KWS and add
keyword information by audio and text cross-attention into both branches. We
perform experiments on our internal dataset and Aishell-1. The results show
that U2-KWS can achieve a significant relative wake-up rate improvement of 41%
compared to the traditional customized KWS systems when the false alarm rate is
fixed to 0.5 times per hour.
