Talking Condition Recognition in Stressful and Emotional Talking Environments Based on CSPHMM2s
arXiv:1706.09729 · doi:10.1007/s10772-014-9251-7
Abstract
This work is aimed at exploiting Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s) as classifiers to enhance talking condition recognition in stressful and emotional talking environments (completely two separate environments). The stressful talking environment that has been used in this work uses Speech Under Simulated and Actual Stress (SUSAS) database, while the emotional talking environment uses Emotional Prosody Speech and Transcripts (EPST) database. The achieved results of this work using Mel-Frequency Cepstral Coefficients (MFCCs) demonstrate that CSPHMM2s outperform each of Hidden Markov Models (HMMs), Second-Order Circular Hidden Markov Models (CHMM2s), and Suprasegmental Hidden Markov Models (SPHMMs) in enhancing talking condition recognition in the stressful and emotional talking environments. The results also show that the performance of talking condition recognition in stressful talking environments leads that in emotional talking environments by 3.67% based on CSPHMM2s. Our results obtained in subjective evaluation by human judges fall within 2.14% and 3.08% of those obtained, respectively, in stressful and emotional talking environments based on CSPHMM2s.
References in corpus (4)
- Speaker Identification in the Shouted Environment Using Suprasegmental Hidden Markov Models
- Studying and Enhancing Talking Condition Recognition in Stressful and Emotional Talking Environments Based on HMMs, CHMM2s and SPHMMs
- 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 (3)
- Novel Dual-Channel Long Short-Term Memory Compressed Capsule Networks for Emotion Recognition
- Speaker Identification in a Shouted Talking Environment Based on Novel Third-Order Circular Suprasegmental Hidden Markov Models
- Emotion Recognition based on Third-Order Circular Suprasegmental Hidden Markov Model