Exploring Representation Learning for Small-Footprint Keyword Spotting
Abstract
In this paper, we investigate representation learning for low-resource keyword spotting (KWS). The main challenges of KWS are limited labeled data and limited available device resources. To address those challenges, we explore representation learning for KWS by self-supervised contrastive learning and self-training with pretrained model. First, local-global contrastive siamese networks (LGCSiam) are designed to learn similar utterance-level representations for similar audio samplers by proposed local-global contrastive loss without requiring ground-truth. Second, a self-supervised pretrained Wav2Vec 2.0 model is applied as a constraint module (WVC) to force the KWS model to learn frame-level acoustic representations. By the LGCSiam and WVC modules, the proposed small-footprint KWS model can be pretrained with unlabeled data. Experiments on speech commands dataset show that the self-training WVC module and the self-supervised LGCSiam module significantly improve accuracy, especially in the case of training on a small labeled dataset.
Cite
@article{arxiv.2303.10912,
title = {Exploring Representation Learning for Small-Footprint Keyword Spotting},
author = {Fan Cui and Liyong Guo and Quandong Wang and Peng Gao and Yujun Wang},
journal= {arXiv preprint arXiv:2303.10912},
year = {2023}
}