  The work of this research is devoted to studying and enhancing talking
condition recognition in stressful and emotional talking environments
(completely two separate environments) based on three different and separate
classifiers. The three classifiers are: Hidden Markov Models (HMMs),
Second-Order Circular Hidden Markov Models (CHMM2s) and Suprasegmental Hidden
Markov Models (SPHMMs). The stressful talking environments that have been used
in this work are composed of neutral, shouted, slow, loud, soft and fast
talking conditions, while the emotional talking environments are made up of
neutral, angry, sad, happy, disgust and fear emotions. The achieved results in
the current work show that SPHMMs lead each of HMMs and CHMM2s in improving
talking condition recognition in stressful and emotional talking environments.
The results also demonstrate that talking condition recognition in stressful
talking environments outperforms that in emotional talking environments by
2.7%, 1.8% and 3.3% based on HMMs, CHMM2s and SPHMMs, respectively. Based on
subjective assessment by human judges, the recognition performance of stressful
talking conditions leads that of emotional ones by 5.2%.
