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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
relevant attempts have not produced successful real world applications. In this paper, we argue that Adaptive Information Filtering is a complex and dynamic problem not easily addressed with Genetic Algorithms and Memetic Algorithms that adopt weighted keyword vector for profile representation. We discuss the theoretical
proposed approach uses a segmental DTW, wherein search is carried out only at syllable boundaries. This reduces the search complexity by 9 times compared to conventional sliding window DTW. The first pass of the proposed method uses a minimum set of templates for a keyword to search through the segmented audio. New templates
We have been developing a system, called Tour Miner, which mines tour plans from SNS. It consists of two functions: mining and smelting. The mining function searches SNS for a given keyword and discovers travel records related to the keyword. The smelting function combines the travel records and extracts tour plans
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