Speaker Model Clustering to Construct Background Models for Speaker Verification

• Autorzy: Dişken G. , Tüfekci Z. , Çevik U.

Identyfikatory

DOI

10.1515/aoa-2017-0014

• Języki publikacji: EN

Abstrakty

EN

Conventional speaker recognition systems use the Universal Background Model (UBM) as an imposter for all speakers. In this paper, speaker models are clustered to obtain better imposter model representations for speaker verification purpose. First, a UBM is trained, and speaker models are adapted from the UBM. Then, the k-means algorithm with the Euclidean distance measure is applied to the speaker models. The speakers are divided into two, three, four, and five clusters. The resulting cluster centers are used as background models of their respective speakers. Experiments showed that the proposed method consistently produced lower Equal Error Rates (EER) than the conventional UBM approach for 3, 10, and 30 seconds long test utterances, and also for channel mismatch conditions. The proposed method is also compared with the i-vector approach. The three-cluster model achieved the best performance with a 12.4% relative EER reduction in average, compared to the i-vector method. Statistical significance of the results are also given.

Słowa kluczowe

EN

Gaussian mixture models; k-means; imposter models; speaker clustering; speaker verification

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2017
• Tom: Vol. 42, No. 1
• Strony: 127--135
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Dişken G.

• Department of Electrical-Electronics Engineering, Adana Science and Technology University, Adana, Turkey

autor

Tüfekci Z.

• Department of Computer Engineering, Çukurova University, Adana, Turkey

autor

Çevik U.

• Department of Electrical-Electronics Engineering, Çukurova University, Adana, Turkey

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).