Abstract
This paper presents a time-aligned singular value decomposition (SVD) analysis for speaker identification. SVD analysis has been used for fast spectral matching based on a global representation of an entire utterance. We incorporate temporal normalization directly into the decomposition by using a dynamic time warping (DTW) path to time-align the rows of the feature matrix prior to SVD analysis. Speaker identification results using the TI-46 database indicates that the time-aligned SVD significantly improves accuracy for most threshold choices.
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