Open vocabulary keyword spotting is a crucial and challenging task in
automatic speech recognition (ASR) that focuses on detecting user-defined
keywords within a spoken utterance. Keyword spotting methods commonly map the
audio utterance and keyword into a joint embedding space to obtain some
affinity score. In this work, we propose AdaKWS, a novel method for keyword
spotting in which a text encoder is trained to output keyword-conditioned
normalization parameters. These parameters are used to process the auditory
input. We provide an extensive evaluation using challenging and diverse
multi-lingual benchmarks and show significant improvements over recent keyword
spotting and ASR baselines. Furthermore, we study the effectiveness of our
approach on low-resource languages that were unseen during the training. The
results demonstrate a substantial performance improvement compared to baseline
methods.

本研究提出了一种名为 AdaKWS 的新型关键词检测方法，通过训练一个文本编码器输出与关键词相关的归一化参数，从而用于处理听觉输入，并在多语言基准测试和低资源语言上展开全面评估，取得了显著的性能改进。