Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data

• URL: http://arxiv.org/abs/2309.00647v1
• Date: Thu, 31 Aug 2023 07:29:42 GMT
• Title: Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data
• Authors: Seunghan Yang, Byeonggeun Kim, Kyuhong Shim, Simyung Chang
• Abstract summary: We propose a framework that uses easily collectible, unlabeled reading speech data as an auxiliary source. We then adopt multi-task learning that helps the model to enhance the representation power from out-of-domain auxiliary data.
• Score: 19.075820340282934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot keyword spotting (FS-KWS) models usually require large-scale annotated datasets to generalize to unseen target keywords. However, existing KWS datasets are limited in scale and gathering keyword-like labeled data is costly undertaking. To mitigate this issue, we propose a framework that uses easily collectible, unlabeled reading speech data as an auxiliary source. Self-supervised learning has been widely adopted for learning representations from unlabeled data; however, it is known to be suitable for large models with enough capacity and is not practical for training a small footprint FS-KWS model. Instead, we automatically annotate and filter the data to construct a keyword-like dataset, LibriWord, enabling supervision on auxiliary data. We then adopt multi-task learning that helps the model to enhance the representation power from out-of-domain auxiliary data. Our method notably improves the performance over competitive methods in the FS-KWS benchmark.

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