Meta-heuristic approach in neural network for stress detection in Marathi speech

Stress is defined as a form of psychalgia. Owing to the current day lifestyle of Homo-sapiens, the most recurring pain is psychogenic; and the most damaging form of psychalgia. Stress in its most severe form, has led to the death of many individuals of this species. In accordance to a study conducted by WHO in 2015, around 800,000 individuals commit suicide each year (one individual per 40 s). The only solution to this conundrum is to bring in efficient mechanized stress detection technique which utilize proven measures and are unbiased, is called “speech emotion recognition” (SER). Stress, by itself, is not an emotion, but gives rise to specific emotions. This paper proposes SER using neural network classifier with weight optimization using fusion of optimization algorithms viz. BAT, genetic algorithm, particle swarm organization and simulated annealing. Classifier is trained using multi-model feature set. Gammatone Wavelet Cepstral coefficient, Mel Frequency Cepstral coefficient, pitch, vocal tract frequency and energy are the features used to identify different emotions. Detect the stress level being main objective SUSAS benchmark database and Marathi language database is used for performance analysis. Performance parameters like cost function for evaluating meta-heuristic optimization algorithm and accuracy of emotion detection is calculated. The overall accuracy of 84.2% of stress related emotions is achieved.