Optimization of TESPAR Features using Robust F-Ratio for Speaker Recognition

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 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