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The total recognition time as well as the memory requirement in speaker recognition is mainly governed by the number of speakers, the number of frame vectors in the test sequence and the feature dimensionality. The adjacent frame vectors can show similarity in the feature space because of the slow movements of the articulators. Hence efficient frame selection techniques to select non-redundant frames...
The amount of speaker specific information in speech signal varies from frame to frame depending on spoken text and environmental conditions. A frame selection at the preprocessing stage can be an added advantage in this context. In pre-quantization (PQ) we select a new sequence of frames Y from the original frames X such that length of Y is less than X. In this paper, we first analyze a number of...
State-of-the-art Speaker Identification (SI) systems use Gaussian Mixture Models (GMM) for modeling speakerspsila data. Using GMM, a speaker can be identified accurately even from a large number of speakers, when model complexity is large. However, lower ordered speaker model using GMM show poor accuracy as lesser number of Gaussian are involved. In SI context, not much attention have been paid towards...
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