Speaker Identification in Each of the Neutral and Shouted Talking Environments Based on Gender-Dependent Approach Using Sphmms

Abstract

It is well known that speaker identification performs extremely well in the neutral talking environments; however, the identification performance is declined sharply in the shouted talking environments. This work aims at proposing, implementing and testing a new approach to enhance the declined performance in the shouted talking environments. The new proposed approach is based on gender-dependent speaker identification using suprasegmental hidden Markov models (SPHMMs) as classifiers. This proposed approach has been tested on two different and separate speech databases: our collected database and the Speech Under Simulated and Actual Stress (SUSAS) database. The results of this work show that gender-dependent speaker identification based on SPHMMs outperforms gender-independent speaker identification based on the same models and gender-dependent speaker identification based on hidden Markov models (HMMs) by about 6 and 8%, respectively. The results obtained based on the proposed approach are close to those obtained in subjective evaluation by human judges.