ISSN : 1796-2048
Volume : 2    Issue : 6    Date : November 2007

Linear Discriminant Analysis F-Ratio for Optimization of TESPAR & MFCC Features for Speaker
K. Anitha Sheela and K. Satya Prasad
Page(s): 34-43
Full Text:
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This paper deals with implementing an efficient optimization technique for designing an Automatic
Speaker Recognition (ASR) System, which uses average F-ratio score of TESPAR(Time Encoded
Signal Processing And Recognition) and MFCC(Mel frequency Cepstral Coefficients) features, to
yield high recognition accuracy even in adverse noisy conditions. A new ranking scheme is also
proposed in order to stabilize the rank of features in various noise levels by taking Arithmetic Mean
of the F-Ratio scores obtained from various levels of Signal to Noise Ratio (SNR). The result is
presented for a Text-Dependent ASR system with 20 speaker database. An RBF (Radial Basis
Function) Neural Network is used for Recognition purpose. Also a comparative study has been
performed for recognition accuracies of optimized MFCC and TESPAR features and we conclude
that new proposed average F-Ratio technique has resulted in better accuracy compared to simple
F-ratio in noisy environment and also we came to know that TESPAR features are more redundant
compared to MFCC.

Index Terms
ASR, F-Ratio, Average F-Ratio, TESPAR, RBF Neural Network, MFCC.