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We explore techniques to improve the robustness of small-footprint keyword spotting models based on deep neural networks (DNNs) in the presence of background noise and in far-field conditions. We find that system performance can be improved significantly, with relative improvements up to 75% in far-field conditions
Facing high error rates and slow recognition speed for full text transcription of unconstrained handwriting images, keyword spotting is a promising alternative to locate specific search terms within scanned document images. We have previously proposed a learning-based method for keyword spotting using character hidden
The Bag-of-Visual-Words (BoVW) approach has been attracted some attention in the field of keyword spotting. However, the BoVW approach discards the spatial relations of the visual words. Therefore, a visual language model is integrated into the BoVW framework in this study so as to add the spatial information. To
The paper proposed a method to realize a speech-to-gesture conversion for communication between normal and speech-impaired people. Keyword spotting was employed to recognize the keywords from input speech signals. At the same time, the three dimensional gesture models of keywords were built by 3D modeling technology
of vocabulary words in the users speech utterance. In this paper, we investigate an approach that can be deployed in keyword spotting systems. We propose a phoneme classifier that will be ultimately used to provide confidence values to be compared against existing Automatic Speech Recognizer word confidences. The end
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