Studying and enhancing talking condition recognition in stressful and emotional talking environments based on HMMs, CHMM2s and SPHMMs

Abstract

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%.