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Depression Detection using BDI, Speech Recognition and Facial Recognition

Prathamesh Raut
2018 International Journal for Research in Applied Science and Engineering Technology  
BDI (Beck Depression Inventory) is test done on computer to detect depression on a certain scale.  ...  For the detection, some android applications are being designed so that using the speech signals mood of a person is determined. Texts, speech inputs, facial expressions are considered.  ...  Algorithms 1) Naïve Bayes: The Machine Learning algorithm used for speech input's result is Naïve Bayes classifier.  ... 
doi:10.22214/ijraset.2018.4062 fatcat:h3zhe3k6tjca5o3lk3tyd7xz4e

On the review of image and video-based depression detection using machine learning

Arselan Ashraf, Teddy Surya Gunawan, Bob Subhan Riza, Edy Victor Haryanto, Zuriati Janin
2020 Indonesian Journal of Electrical Engineering and Computer Science  
This review is based on the image and video-based depression detection model using machine learning techniques. This paper analyses the data acquisition techniques along with their databases.  ...  With the mounting evidence, machine learning has the capability to detect mental distress like depression.  ...  ACKNOWLEDGMENTS The author would like to express their gratitude to the Malaysian Ministry of Education (MOE), which has provided research funding through the Fundamental Research Grant, FRGS19-076-0684  ... 
doi:10.11591/ijeecs.v19.i3.pp1677-1684 fatcat:4vv2a4a4mrdw5nlusglkp6eoum

Emotion and mental state recognition from speech

Julien Epps, Roddy Cowie, Shrikanth Narayanan, Björn Schuller, Jianhua Tao
2012 EURASIP Journal on Advances in Signal Processing  
, machine learning, artificial intelligence and signal processing, among others.  ...  As research in speech processing has matured, attention has gradually shifted from linguistic-related applications such as speech recognition towards paralinguistic speech processing problems, in particular  ...  , machine learning, artificial intelligence and signal processing, among others.  ... 
doi:10.1186/1687-6180-2012-15 fatcat:cke4oqstqvepfpzn7cuogbucvm

Mental Health Detection from Speech Signal: A Convolution Neural Networks Approach

Haizhen An, Xiaoyong Lu, Daimin Shi, Jingyi Yuan, Renjun Li, Tao Pan
2019 2019 International Joint Conference on Information, Media and Engineering (IJCIME)  
In the absence of depressed speech corpus, the authors regard depression as a negative emotion, and build the model by Convolution Neural Networks (CNNs), a machine learning method for detecting mental  ...  The objective and automated detecting of mental health using speech signal has become popular.  ...  From the machine learning standpoint, depression detection can be considered as a regression or classification problem.  ... 
doi:10.1109/ijcime49369.2019.00094 fatcat:e2ux7j3um5ffdjacman6sea5jq

Application of Various Machine Learning Techniques in Sentiment Analysis for Depression Detection

On considering these factors, it is planned to perform a survey on the application of various machine learning techniques that have been used in the domain of sentimental analysis for depression detection  ...  Although many researchers explored numerous techniques in predicting depression, still there is no improvement and the generations are facing higher rate of depression.  ...  The researchers had tried adding an automatic speech recognition system into depression detection system [8] .  ... 
doi:10.35940/ijitee.j1052.08810s19 fatcat:gzvp4p5hcjcb3e7uxotzt6qu2u

A depression recognition method for college students using deep integrated support vector algorithm

Yan Ding, Xuemei Chen, Qiming Fu, Shan Zhong
2020 IEEE Access  
This study uses text-level mining of Sina Weibo data from college students to detect depression among college students.  ...  First, collect text information of college student users in Sina Weibo, and construct the text information into input data that can be used for machine learning.  ...  BASED ON MACHINE LEARNING At present, the commonly used depression recognition models based on machine learning are Gradient Boosting Decision Tree(GBDT) [25] , K-Nearest Neighbor (KNN) [26] , Support  ... 
doi:10.1109/access.2020.2987523 fatcat:anapeh3q6rakznhfnuzl7jbfuq

Advances in Emotion Recognition: Link to Depressive Disorder [chapter]

Xiaotong Cheng, Xiaoxia Wang, Tante Ouyang, Zhengzhi Feng
2020 Mental Disorders [Working Title]  
The emotion recognition algorithms using emotion representation based on emotional labels are intuitive which are ambiguous for computer processing.  ...  Emotion recognition enables real-time analysis, tagging, and inference of cognitive affective states from human facial expression, speech and tone, body posture and physiological signal, as well as social  ...  Textual emotion recognition With the growing amount of emotional information from social media, including text, photos, and videos, emotion recognition through multimodal information using machine learning  ... 
doi:10.5772/intechopen.92019 fatcat:jmss4llbpnfrxcue6bzebsgmby

AudVowelConsNet: A phoneme-level based deep CNN architecture for clinical depression diagnosis

Muhammad Muzammel, Hanan Salam, Yann Hoffmann, Mohamed Chetouani, Alice Othmani
2020 Machine Learning with Applications  
On the other hand, research in Machine Learning-based automatic recognition of depression from speech focused on the exploration of various acoustic features for the detection of depression and its severity  ...  In this paper, we propose an Artificial Intelligence (AI) based application for clinical depression recognition and assessment from speech.  ...  On the other hand, research in Machine Learning-based automatic recognition of depression from speech focused on the exploration of various acoustic features for the detection of depression and its severity  ... 
doi:10.1016/j.mlwa.2020.100005 fatcat:q5f6pdncijbihbll6mvrlqtcqa

Speech depression recognition based on attentional residual network

Xiaoyong Lu, Daimin Shi, Yang Liu, Jingyi Yuan
2021 Frontiers in Bioscience-Landmark  
Aiming at the problem of the complex structure of the deep neural network method used in the recognition of speech depression and the traditional machine learning methods need to manually extract the features  ...  Experimental results show that compared with traditional machine learning methods, this model obtains better results in the recognition of depression, which can meet the need for actual recognition application  ...  Aiming at the problem of the complex structure of the deep neural network method used in the recognition of speech depression and the traditional machine learning methods need to manually extract the features  ... 
doi:10.52586/5066 pmid:34994187 fatcat:j6ifeqw2jbge7jzme35dooazue

A Novel Smart Depression Recognition Method Using Human-Computer Interaction System

Lijun Xu, Jianjun Hou, Jun Gao, Shan Zhong
2021 Wireless Communications and Mobile Computing  
After the user inputs his own voice, the final recognition result is output through the recognition of the network model used in this research.  ...  The main contributions of this research are (1) the use of an audio depression regression model (DR AudioNet) based on a convolutional neural network (CNN) and a long-short-term memory network (LSTM) to  ...  And we will also consider to use multiview learning, transfer learning, and the other advanced machine learning technologies to further develop the performance of the proposed intelligent recognition of  ... 
doi:10.1155/2021/5565967 fatcat:drfifkv6hjad5jcprhghbv4vhq

Depression Detection using speech as Input Signal

Aniket Waghela, Prinkle Singharia, Bhavya Haria, Bhakti Sonawane
2020 Zenodo  
It is focusing on two aspects: gathering conversations and getting only the patient's audio and creating a deep learning model for automatic depression detection.  ...  Convolutional Neural Network (CNN) classifier is used to find patterns in audio characteristics of the depressed patients.  ...  Machine Learning concepts can use voice and speech as input to carry out various types of analysis, one of them being using voice and speech signals to classify between depressed and non-depressed patients  ... 
doi:10.5281/zenodo.4166324 fatcat:su3l2z63bjgbfeo6lfszfqg2jm

Speech Based Depression Detection using Convolution Neural Networks

Recent studies have shown that the speech emotion analysis can effectively be used in distinguishing emotional features and a depressed speech varies from that of a normal speech to a great extent.  ...  The long term ignorance of the illness may worsen the mental health of the one suffering from it. Thus the early diagnosis of depression is of great significance.  ...  He Lang, and Cui Cao [6] uses a combination of handcrafted and deep learned features which can effectively measure the severity of depression from speech was used.  ... 
doi:10.35940/ijitee.i7076.079920 fatcat:mjnswo4gxrfb3brwepg25j3ztq

A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health

Xin Chen, Zhigeng Pan
2021 International Journal of Environmental Research and Public Health  
It has been proved that it can be used in the diagnosis of depression. The voice data used for modeling in this study adopted the authoritative public data set, which had passed the ethical review.  ...  At present, the diagnosis of depression mainly depends on the interviews between doctors and patients, which is subjective, slow and expensive.  ...  [11] , using 4930 speech data of 170 samples, the authors extracted 1582 dimensional features and used machine learning to recognize depression.  ... 
doi:10.3390/ijerph18126441 fatcat:dy4nde4mufgd3e7thmf2go435m

Depression Detection using Speech Recognition, BDI and Image Processing

Madhura Deshpande
2019 International Journal for Research in Applied Science and Engineering Technology  
Facial expressions are detected by using Image Processing. Then depending on the result doctors or the experts examine the type of mood whether happy, depressed or sad and give suggestions.  ...  Depression is more than just a low moodit's a serious condition that can be treated as a mental health issue. For the detection of depression, some techniques are used.  ...  Machine learning algorithms are used for recognition and classification of different classes of face emotions by training of different set of images.  ... 
doi:10.22214/ijraset.2019.2020 fatcat:6wqkbd5rvzfezkig5q3d4m5m7y

Depression Scale Recognition over Fusion of Visual and Vocal Expression using Artificial Intellectual Method

Pratibha Gayke Shinde, Assistant Professor, Department of Information Technology, Dr. Vithalrao Vikhe Patil College of Engineering, Ahmednagar, India., Rohini S Kulkarni, Department of Computer Science and Engineering, K.L.E. Institute of Technology, Hubli, Visvesvaraya Technological University, Karnataka, India.
2021 International journal of recent technology and engineering  
This comprehensive evaluation of existing methodologies focuses on machine learning (ML) algorithm and image processing (IP) algorithm, as documented in over sixty articles over the last ten years.  ...  visual and verbal cues alone or in combination with signals for automated depression evaluation The suggested work also used deep learning and natural language processing to estimate depression levels  ...  On user level, we conducted a similar evaluation of some of the most extensively used deep learning techniques and its algorithms for depression identification from tweets.  ... 
doi:10.35940/ijrte.b6402.0910321 fatcat:j7vrlpodlfhfxkol25ace5rz2e
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