Speaker Model Clustering to Construct Background Models for Speaker Verification
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Tarih
2017
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Polska Akad Nauk, Polish Acad Sciences, Inst Fundamental Tech Res Pas
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Gaussian mixture models, k-means, imposter models, speaker clustering, speaker verification
Kaynak
Archives of Acoustics
WoS Q Değeri
Q3
Scopus Q Değeri
Q3
Cilt
42
Sayı
1
