Stress Level Classification of Speech Using Euclidean Distance Metrics in a Novel Hybrid Multi-Dimensional Feature Space

Presently, automatic stress detection methods for speech employ a binary decision approach, deciding whether the speaker is or is not under stress. Since the amount of stress a speaker is under varies and can change gradually, a reliable stress level detection scheme becomes necessary to accurately assess the condition of the speaker. Such a capability is pertinent to a number of applications, such as for those personnel in law enforcement positions. Using speech and biometric data collected from a real-world, variable-stress level law enforcement training scenario, this study illustrates two methods for automatically assessing stress levels in speech using a hybrid multi-dimensional feature space comprised of frequency-based and Teager energy operator-based features. The first approach uses a nearest neighbor-type clustering scheme at the vowel token level to classify speech data into one of three levels of stress, yielding an overall error rate of 50.5%. The second approach employs accumulated Euclidean distance metric weighting at the sentence-level to yield a relative improvement of 12.1% in performance