Hanilçi, CemalCeliktas, Havva2021-03-202021-03-202018978-1-5386-1501-02165-0608https://hdl.handle.net/20.500.12885/89826th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEYi-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.trinfo:eu-repo/semantics/closedAccessTurkish speaker recognitioni-vectorPLDATurkish Text-Dependent Speaker Verification using i-vector/PLDA ApproachConference ObjectWOS:000511448500458N/AN/A