Speaker Identification in the Shouted Environment Using Suprasegmental Hidden Markov Models
Abstract
In this paper, Suprasegmental Hidden Markov Models (SPHMMs) have been used to enhance the recognition performance of text-dependent speaker identification in the shouted environment. Our speech database consists of two databases: our collected database and the Speech Under Simulated and Actual Stress (SUSAS) database. Our results show that SPHMMs significantly enhance speaker identification performance compared to Second-Order Circular Hidden Markov Models (CHMM2s) in the shouted environment. Using our collected database, speaker identification performance in this environment is 68% and 75% based on CHMM2s and SPHMMs respectively. Using the SUSAS database, speaker identification performance in the same environment is 71% and 79% based on CHMM2s and SPHMMs respectively.
Cite
@article{arxiv.1706.09691,
title = {Speaker Identification in the Shouted Environment Using Suprasegmental Hidden Markov Models},
author = {Ismail Shahin},
journal= {arXiv preprint arXiv:1706.09691},
year = {2017}
}