Abstract Background In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson’s, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician’s experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. Methods This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. Results A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey – Kramer test. Conclusion The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.

An insight into diagnosis of depression using machine learning techniques: a systematic review

Simulation of psychological course satisfaction based on android mobile system and neural network

The invention discloses a cross-language emotional speech synthesis method and system. The method comprises steps of establishing a context-dependent label format and a context-dependent clustering problem set; determining a first language annotation document, a second language annotation document, a target emotional Putonghua annotation document, an annotation document to be synthesized, a first language acoustic parameter, a second language acoustic parameter, a target emotional acoustic parameter; based on the first language annotation document, the second language annotation document, the target emotional Putonghua annotation document, the first language acoustic parameter, the second language acoustic parameter, and the target emotional acoustic parameter, determining the multi-speaker target emotional average acoustic model; and finally, inputting the annotation document to be synthesized into the multi-speaker target emotional average acoustic model to obtain a first language or / and a second language target emotional speech synthesis document to realize the synthesis of the same speaker or a different speaker cross-language emotional voice.

Cross-language emotional speech synthesis method and system

Depression or depressive disorder is a mental disorder that is prevalent in current times. Some of the time people feel down or low in their lives. However, when this inclination perseveres for longer time, it influences general intellectual capacity of the mind. It may prompt some genuine cerebrum working handicaps, for example, alarm assaults, tension, dread and so on. Wretchedness is not quite the same as misery/pain. It is regularly accepted that downturn is a consequence of substance awkwardness however that doesn't catch the unpredictability of the illness. Moreover, misery contributes significantly to the worldwide weight of malady and impacts individuals in each and every society around the globe. While the worldwide weight of misery presents a significant challenge to general wellbeing, at the clinical level, at the financial level, and at the social level, there are several evidence-based and distinct approaches that can efficiently tackle or reduce this burden. Understanding mechanisms that contribute to chronic impairment induced by significant depressive disorder is still not known. Therefore, in recent years, Machine Learning (ML) methods have appeared as interesting techniques to address such complicated issues. This study describes the current functionality procedures of distinct methods, including some models with distinct parameters, benefits, and drawbacks. The primary focus of this paper is to underscore a relative examination of methods that can be used for overall identification and assessment of depression. The purpose of this study is to present a better and more effective assessment of different methods to propose the best procedure from all current methods. Moreover, this research work presents comparison of the features and techniques that are used in prediction of depression. Also, the point of this paper is to explore to accomplish better customized treatment for the indications of melancholy.

https://www.jetir.org/papers/JETIR2008385.pdf

An Evaluation of Machine Learning Techniques in Predicting Depression using Motion and Facial Expressions

Clinical depression can be characterized by a range of psychological factors, resulting in social, occupational and educational impaired function. Current clinical practice depends almost exclusively on self-report and clinical opinion, risking a range of subjective biases. Such methods are subjective and single in nature, and lack an objective predictor of depression. This project aims at developing a novel method for diagnosis of depression using speech analysis from psychological perspective. It is well known that the Self is not only the cognitive subject, but also the core of personality. In this PhD work, for above reason, classical scientific psychology paradigms are employed on abnormalities of self-related processing in patients from different dimensions of the Self, and speech signal processing methods and Machine Learning methods are adopted for depressed speech. We believe the method can better capture psychological characteristics of depressed patients, and make a meaningful progress in improving diagnosis accuracy.

A novel method design for diagnosis of psychological symptoms of depression using speech analysis

Happiness has attracted much attention of the researchers in various fields. This paper realizes prosodic conversion of emotional speech for happiness computing on speech communication. An emotional speech corpus includes 11 kinds of typical emotional utterances is designed, where each utterance is labeled the emotional information with PAD value in a psychological sense. A five-scale tone model is employed to model the pitch contour of emotional utterances on the syllable level. A generalized regression neural network (GRNN) based prosody conversion model is built to realize the transformation of pitch contour, duration and pause duration of emotional utterance, in which the PAD values of emotion and context parameter are adopted to predict the prosodic features. Emotional utterance is then re-synthesized with the STRAIGHT algorithm by modifying pitch contour, duration and pause duration. Experimental results on Emotional Mean Opining Score (EMOS) demonstrate that the prosody conversion effect of proposed method can express corresponding feelings.

Aibao Zhou

Papers

A novel method design for diagnosis of psychological symptoms of depression using speech analysis

Applying PAD three dimensional emotion model to convert prosody of emotional speech