Assimilate the auditory scale with wavelet packet filters for multistyle classification of speech under stress

Nowadays, people are having high stress level due to highworkload stress, emergency phone call and multitasking. Emotional/stress of a person affects his/her performance in daily life and speech production. The research for understanding the human emotional/stress states using speech has undergone research and development in the past two decades. This paper presents a feature extraction method based on wavelet packet decomposition for detecting the emotional or stressed states of the person. Three different wavelet packet filter bank structures are design based on Bark scale, Mel Scale and Equivalent Rectangular Bandwidth (ERB) Scale. Linear Discriminant Analysis (LDA) based classifier and Support Vector Machine (SVM) are employed as classifier to identify the emotional/stressed states of a person. In this study speech samples are taken from Speech Under Simulated and Actual Stress (SUSAS) database. Experimental result shows that the suggested method can be used to identify the stress and emotional state of a person.