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- W2389674672 abstract "This paper presents an approach for text--independent speaker identification based onstatistical inference and vector quantization technique, introduces the system TISI (Text--IndependentSpeaker Identification) in which proposed approach is implemented. The statistics basis of the TISIbased on vector quantization is discussed, the effect of the sample quantity of testing speech on thecorrect rate is analyzed, and the formula for calculating the discriminatory ability of each componentof feature vector is given. First, for each of the registered speakers, the speaker's acoustic featurevector is extracted from training speech by Perceptually based Linear Predictive (PLP) analysis,then, The LBG algorithm for the VQ is used to get the clusters in the feature set and the statisticalcharacteristics of each code word is calculated. Finally, the method of statistical inference is used todetect the speaker identity. The experiments have shown that the lower order parts of the PLPcoefficients have more speaker individual information compared with that of higher order parts. Foraround 60 seconds testing sessions within 50 speakers, the correct rate of speaker identificationcloses to 99 %." @default.
- W2389674672 created "2016-06-24" @default.
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- W2389674672 date "1998-01-01" @default.
- W2389674672 modified "2023-09-24" @default.
- W2389674672 title "AN APPROACH FOR TEXT-INDEPENDENT SPEAKER IDENTIFICATION BASED ON STATISTICAL INFERENCE AND VECTOR QUANTIZATION TECHNIQUE" @default.
- W2389674672 hasPublicationYear "1998" @default.
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