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.
@article{arxiv.2309.08561,
title = {Open-vocabulary Keyword-spotting with Adaptive Instance Normalization},
author = {Aviv Navon and Aviv Shamsian and Neta Glazer and Gill Hetz and Joseph Keshet},
journal= {arXiv preprint arXiv:2309.08561},
year = {2023}
}