Performance analysis of Gaussian mixture model speaker recognition systems with different speaker features

Автор: Akira Kurematsu , Mariko Nakano-Miyatake , Hector Perez-Meana , Eric Simancas Acevedo

Статья в выпуске: т.5, 2005 года.

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This paper analyzes the effect of the speaker feature vector characteristics, in the performance of speaker recognition systems (SRS) based on the Gaussian Mixture Model (GMM). To this end, the performance of the SRS is analized using speaker features derived from: a) linear predictive cepstral coefficients (LPCepstral) extracted from the whole speech frame, b) LPCepstral derived from the voiced parts of the speech frame, c) LPCepstral extracted from voiced segments of speech frame together with the pitch information, d) LPCepstral extracted from voiced segments of each frame normalized using a Cepstral Mean Normalization (CMN). Evaluation results, using phrases of 2.5-3 second of telephone speech utterances in Japanese language, show that a fairly good performance of GMM-based SRS is achieved with most speaker features vectors with both, close test as well as with open-test, although the features vector providing the best recognition performance closely depends on each particular speaker.

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Короткий адрес: https://sciup.org/14316017

IDR: 14316017

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