Clinical Depression Analysis Using Speech Features

Abstract

Depression is a most common severe mental disturbance health disorder causing high societal costs. In clinical practice rating for depression depends almost on self questionnaires and clinical patient history report opinion. In recent years, the awareness has generated for automatic detection of depression from the speech signal. Some queries are raised that which features are more responsible for depression from speech and which classifiers gives good results. By identifying proper features from speech signal system even one can save the life of a patient. In this paper, a survey of speech signal features which relates for depression analysis is presented. Specially focused on adolescence speech. After surveying it is hypothesized that many speech features are there which are responsible for depression like linear features Prosodic, cepstral, spectral and glottal features and non-linear feature Teager energy operator (TEO). Some classification methods for depression analysis from previous studies are summarized.

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm

“…The third step is to choose the best communication technique because the doctors have successfully found an emotionally supportive network. The K-mean and the support vector machine (SVM) model were recently employed to carry out the order [ 8 , 9 ]. Component determination measures a very efficient pursuit calculation when required, which generates considerable computational expenditure, and information gathering measures their combined subjects to comprehend sections, image portrayals, and meetings.…”

“…By providing dialogue at opportune times, the audio depression approach can help many more individuals than the traditional intelligence system. Numerous forms of uncertainty will be a barrier if an audio device is intended to identify a person’s depressed condition [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. By giving the proper data to all audio devices, which will further decrease functional loss, the problem of uncertainties in the design process will be reduced.…”

“…The major limitation of the existing approach [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ] for identifying the depressed state of an individual is that only wiring-based module components are present; the components are directly connected to display units that provide the state of a person using a three-fold pattern, such as normal, abnormal and indeterminate. However, by using the three-fold pattern, it is not possible to provide quick decisions as the characteristics of an individual are not defined and it is impossible to understand the type of emotional state.…”

A hybrid KNN-MLP algorithm to diagnose bipolar disorder

“…In clinical practice rating for depression depends almost on self-questionnaires and clinical patient history report opinion [17]. Depression is a feature of some psychiatric syndromes such as bipolar disorder but it may also be a normal reaction to life events such as bereavement, a symptom of some bodily ailments or a side effect of some drugs and medical treatments [18].…”