Turkish Text-Dependent Speaker Verification using i-vector/PLDA Approach

Küçük Resim Yok

Tarih

2018

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

Ieee

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

i-vector feature extraction is the state-of-the-art technique for text-independent speaker recognition. There exist studies in literature utilizing i-vector approach for text-dependent speaker verification. However, its performance for Turkish speaker recognition remains unknown. In this study, the performance of i-vector approach is analysed on Turkish text-dependent speaker recognition database consisting of 59 speakers. Experimental results show that, traditional Mel-frequency cepstral coefficients modelled with Gaussian mixture model - universal background model (GMM-UBM) outperforms i-vector system. It is also observed that probabilistic linear discriminant analysis (PLDA) classifier using i-vector features does not bring any performance improvement over the standard cosine distance scoring (CDS) for Turkish text-dependent speaker verification.

Açıklama

26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEY

Anahtar Kelimeler

Turkish speaker recognition, i-vector, PLDA

Kaynak

2018 26Th Signal Processing And Communications Applications Conference (Siu)

WoS Q Değeri

N/A

Scopus Q Değeri

N/A

Cilt

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