Deep Residual Learning for Small-Footprint Keyword Spotting

We explore the application of deep residual learning and dilated convolutions to the keyword spotting task, using the recently-released Google Speech Commands Dataset as our benchmark. Our best residual network (ResNet) implementation significantly outperforms Google's previous convolutional neural networks in terms of accuracy. By varying model depth and width, we can achieve compact models that also outperform previous small-footprint variants. To our knowledge, we are the first to examine these approaches for keyword spotting, and our results establish an open-source state-of-the-art reference to support the development of future speech-based interfaces.Comment: Published in ICASSP 201

A Formal Definition for Configuration

There exists a wide set of techniques to perform keyword-based search over relational databases but all of them match the keywords in the users' queries to elements of the databases to be queried as first step. The matching process is a time-consuming and complex task. So, improving the performance of this task is a key issue to improve the keyword based search on relational data sources.In this work, we show how to model the matching process on keyword-based search on relational databases by means of the symmetric group. Besides, how this approach reduces the search space is explained in detail

Automatically linking MEDLINE abstracts to the Gene Ontology

Much has been written recently about the need for effective tools and methods for mining the wealth of information present in biomedical literature (Mack and Hehenberger, 2002; Blagosklonny and Pardee, 2001; Rindflesch et al., 2002)—the activity of conceptual biology. Keyword search engines operating over large electronic document stores (such as PubMed and the PNAS) offer some help, but there are fundamental obstacles that limit their effectiveness. In the first instance, there is no general consensus among scientists about the vernacular to be used when describing research about genes, proteins, drugs, diseases, tissues and therapies, making it very difficult to formulate a search query that retrieves the right documents. Secondly, finding relevant articles is just one aspect of the investigative process. A more fundamental goal is to establish links and relationships between facts existing in published literature in order to “validate current hypotheses or to generate new ones” (Barnes and Robertson, 2002)—something keyword search engines do little to support