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The authors present some experiments that show the capabilities of using recurrent neural networks (RNNs) in conjunction with hidden Markov models (HMMs) in the context of keyword spotting (KWS): the automatic recognition of a small set of keywords as they occur in unconstrained speech and/or noise. KWS is usually
This paper presents a keyword spotting system based on the NSHP-HMM. This model allows to dynamically create global word models from letters models, and do not require any writing segmentation. The second section describes our system and its application to a keyword-based handwritten mail sorting task. Next section
Two methods are presented to improve word confidence scores for Line-Level Query-by-String Lexicon-Free Keyword Spotting (KWS) in handwritten text images. The first one approaches true relevance probabilities by means of computations directly carried out on character lattices obtained from the lines images considered
functionally important membrane protein; they are hard to collected from the existing databases for slow progress of protein annotations, transitive annotation problem as well as low sequence similarity among them. Our preliminary system was designed with a user-friendly web interface and provides: 1) keywords retrieval against
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