Open-Vocabulary Keyword-Spotting with Adaptive Instance Normalization

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 affini...

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Bibliographic Details
Published inICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 11656 - 11660
Main Authors Navon, Aviv, Shamsian, Aviv, Glazer, Neta, Hetz, Gill, Keshet, Joseph
Format Conference Proceeding
LanguageEnglish
Published IEEE 14.04.2024
Subjects
Acoustics
Adaptation models
adaptive instance normalization
Benchmark testing
open vocabulary
Signal processing
Task analysis
Training
user-defined keyword spotting
Vocabulary
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP48485.2024.10446912