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.

References in corpus (3)

• Speaker Identification in the Shouted Environment Using Suprasegmental Hidden Markov Models
• Enhancing speaker identification performance under the shouted talking condition using second-order circular hidden Markov models
• Employing Second-Order Circular Suprasegmental Hidden Markov Models to Enhance Speaker Identification Performance in Shouted Talking Environments

Cited by in corpus (1)

• Employing Second-Order Circular Suprasegmental Hidden Markov Models to Enhance Speaker Identification Performance in Shouted Talking Environments