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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
the userpsilas acoustic signal from a singing voice and retrieves the music information using both lyrics and melody information. The lyrics recognition module uses a keyword spotting system based on text-content of the lyrics by an HMM comparison engine. The melody recognition module extracts pitch and MFCC features
This paper describes ICSI's 2005 speaker recognition system, which was one of the top performing systems in the NIST 2005 speaker recognition evaluation. The system is a combination of four sub-systems: 1) a keyword conditional HMM system, 2) an SVM-based lattice phone n-gram system, 3) a sequential nonparametric
and converts it into routing keywords. Accent identification is the most important factor for improving the performance of natural language call-routing systems because accents vary widely, even within the same country or community. This variation occurs when non-native speakers start to learn a second language; the
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