Voice Identification Using Nonparametric Density Matching
Text-independent speaker recognition is often based on the premise that acoustic measurements derived from the speech utterances of an individual are characterized by stable, speaker-unique probability density functions (PDFs). This chapter describes a method of comparing speech utterances to determine whether or not the underlying PDFs are the same, hence likely to have been spoken by the same person. The method is independent of assumptions about the form of the PDFs. Based on a conjecture regarding the local relationship between probability density and nearest-neighbor distance, the algorithm is shown to measure global differences between the speakers’ underlying feature distributions. Experimental results are presented for the King telephone database.
KeywordsCovariance Expense Hunt Deconvolution Acoustics
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