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

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.Comment: Interspeech 202

Graph Matching Based Authorization Model for Efficient Secure XML Querying

XML is rapidly emerging as a standard for data representation and exchange over the World Wide Web and an increasing amount of sensitive business data is processed in the XML format. Therefore, it is critical to have control mechanisms to restrict a user to access only the parts of XML documents that he/she is authorized to access. In this paper, we propose the first DTD-based access control model that employs graph matching to analyze if an input query is fully acceptable, fully rejectable, or partially acceptable, and to rewrite for partially acceptable queries only if necessary, along with the features of optimization and speed-up for query rewriting by introducing an index structure. 1