  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.
