Towards the search of detection in speech-relevant features for stress

Most of the parameters proposed for the characterization of the emotion in speech concentrate their attention on phonetic and prosodic features. Our approach goes beyond trying to relate the biometrical signature of voice with a possible neural activity that might generate alterations in voice production. A total of 68, acoustical, glottal and biomechanical parameters were extracted from neutral and stressed speeches. The importance of the parameters was evaluated using t-test, entropy, Receiver Operator Characteristic ROC and Wilcoxon methods and support vector machines algorithms for classification. The emotion under study is the stress produced when a speaker has to defend an idea opposite to his/her thoughts or feelings, and this stress is compared to self-consistent speech. The results show tremor in the vocal folds to be the most relevant feature.