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

摘要

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.