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user, it can be utilized as a trigger. In this paper, we propose a voice trigger system using a keyword-dependent speaker recognition technique. The voice trigger must be able to perform keyword recognition, as well as speaker recognition, without using computationally demanding speech recognizers to properly trigger a
This paper presents a system for keyword detection in spontaneous speech. Keywords are predefined through a set of acoustic examples provided by the users. Keyword detection proceeds in two steps: keyword searching and verification. To address the problem of using the same phoneme models in both keyword and filter
similarities between posteriorgrams. In addition to deriving the lower-bound estimate, we show how it can be efficiently used in an admissible K nearest neighbor (KNN) search for spotting matching sequences. We quantify the amount of computational savings achieved by performing a set of unsupervised spoken keyword spotting
keyword, and terminated in similar fashion with de-activation keyword. Speaker recognition is performed on the activation keyword to allow personalization of the voice commands available to the particular user, who in this scenario is a member of the household. A separate setting is also devised to enable guest user to have
Cepstral Coefficients(FBCC) is used in this paper. Here, from the spoken example of a keyword, segmental Dynamic Time Warping is used to compare the Gaussian Posteriorgrams, which are created from the FBCC feature vector. The keyword detection result obtained using MediaEval 2012 database shows that this system outperforms
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