Open-vocabulary Keyword-spotting with Adaptive Instance Normalization
Aviv Navon Aviv Shamsian Neta Glazer Gill Hetz Joseph Keshet
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.