“…The results of the studies conducted using the TIMIT database are the most widely available and thoroughly described, and so a comparison to other ASR systems using this database is described in detail in Section 4.5 . In the present work, among others, the following were used for distinguishing features: MFCC [ 4 , 5 , 6 , 7 ] and power normalization cepstral coefficients (PNCC) [ 7 ]. In contrast, speaker classification in the present ASR System was based on GMM-UBM [ 4 ], latent factor analysis with Support Vector Machine (LFA-SVM) [ 5 ], Linear Discriminant Analysis (LDA) [ 6 ], Gaussian probabilistic LDA (GPLDA) [ 6 ], and extreme learning machine (ELM) [ 7 ].…”