  Emotions(like fear,anger,sadness,happiness etc.) are the fundamental features
of human behavior and governs his/her mental health. The subtlety of emotional
fluctuations can be examined through perturbation in conversations or speech.
Analysis of emotional state of a person from acoustical features of speech
signal leads to discovery of vital cues determining his or her mental health.
Hence, it's an important field of research in the area of Human Computer
Interaction(HCI). In a recent work we have shown that how the contrast in
Hurst-Exponent calculated from the non-stationary and nonlinear aspects of
"angry" and "sad" speech(spoken in English language) recordings in the
Toronto-Emotional-Speech-Set(TESS) can be used for early detection and
diagnosis of Alzheimer's Disease. In this work we have extended the work and
extracted Hurst-exponent for the speech-signals of similar emotions but spoken
in German language. It has been observed that the Hurst-exponent efficiently
segregates the contrasting emotions of "anger" and "sadness" in the speech
spoken in German language, in similar fashion it has been doing for English
speech. Hence it can be concluded that the Hurst-exponent can differentiate
among speech spoken out of different emotions in language-independent manner.
We propose algorithm for a language-independent application for early
non-invasive detection of various severe neurocognitive-disorders like
Alzheimer's Disease, MND(motor-neuron-disorder), ASD(autism-spectrum-disorder),
depression, suicidal-tendency etc. which is not possible with the state of the
art medical science.
