Stress speech recognition using support vector machine and random forest Jena Bhagyalaxmi1, Jena Akankshya2, Singh Sudhansu Sekhar3 1Assistant Professor, Dept. of Electronics and Communication, Silicon Institute of Technology, Bhubaneswar, Odisha 2Dept. of Electronics and Communication, Silicon Institute of Technology, Bhubaneswar, Odisha 3Professor, School of Electronics Engineering, KIIT University, Bhubaneswar, Odisha, India Online published on 16 October, 2018. Abstract Background/Objectives The Purpose of the study stress speech analysis using support vector machine and random forest algorithms and also compare the performance of the two methods for efficient stress speech recognition. The reason for the development and improvement of speech stress recognition systems is wide usability in nowadays automatic voice controlled systems. Method/Statistical Analysis This study uses a database of 200 different speech samples collected randomly from different individuals for the same sentence, “The weather is too hot today.” We get training dataset and test dataset which helps in the analysis of the signal in accordance with both the algorithms. Findings The efficiency of Support Vector Machine and Random Forest in stress speech recognition. Improvements/Applications Extremely useful in healthcare centers, Psychiatry treatment centers and helpful for psychological improvement of individuals Top Keywords Speech signal, Support Vector Machine (SVM), Random Forest(RF). Top |