Speaker Identification in the Shouted Environment Using Suprasegmental Hidden Markov Models
arXiv:1706.09691 · doi:10.1016/j.sigpro.2008.05.012
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.
References in corpus (1)
Cited by in corpus (8)
- Employing both Gender and Emotion Cues to Enhance Speaker Identification Performance in Emotional Talking Environments
- Talking Condition Recognition in Stressful and Emotional Talking Environments Based on CSPHMM2s
- Studying and Enhancing Talking Condition Recognition in Stressful and Emotional Talking Environments Based on HMMs, CHMM2s and SPHMMs
- Employing Second-Order Circular Suprasegmental Hidden Markov Models to Enhance Speaker Identification Performance in Shouted Talking Environments
- Employing Emotion Cues to Verify Speakers in Emotional Talking Environments
- Speaker Identification in a Shouted Talking Environment Based on Novel Third-Order Circular Suprasegmental Hidden Markov Models
- Speaker Identification Investigation and Analysis in Unbiased and Biased Emotional Talking Environments
- Speaker Identification in each of the Neutral and Shouted Talking Environments based on Gender-Dependent Approach Using SPHMMs