Speaker Identification in the Shouted Environment Using Suprasegmental Hidden Markov Models
This work addresses speaker identification for noisy or stressed speech scenarios, representing an incremental improvement in a domain-specific application.
The paper tackled speaker identification in shouted environments using Suprasegmental Hidden Markov Models (SPHMMs), achieving performance improvements from 68% to 75% on a collected database and from 71% to 79% on the SUSAS database compared to Second-Order Circular Hidden Markov Models.
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.