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2020 Index IEEE Journal of Selected Topics in Signal Processing Vol. 14

2020 IEEE Journal on Selected Topics in Signal Processing  
., +, JSTSP Aug. 2020 997-1011 Automatic Assessment of Depression From Speech via a Hierarchical Attention Transfer Network and Attention Autoencoders.  ...  ., +, JSTSP Feb. 2020 272-281 Automatic Assessment of Depression From Speech via a Hierarchical Atten- tion Transfer Network and Attention Autoencoders.  ... 
doi:10.1109/jstsp.2020.3029672 fatcat:6twwzcqpwzg4ddcu2et75po77u

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.  ...  CONVOLUTION NEURAL NETWORKS Convolutional Neural Networks is one of the representative algorithms of deep learning. Its structure is based on biological visual perception mechanism [23] .  ... 
doi:10.1109/ijcime49369.2019.00094 fatcat:e2ux7j3um5ffdjacman6sea5jq

Speech Based Depression Detection using Convolution Neural Networks

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
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.  ...  With the emergence of neural networks and pattern recognition, many researchers have put effort in detecting depression by analysing non-verbal cues, such as facial expressions, gesture, body language  ...  Speech Based Depression Detection using Convolution Neural Networks II.  ... 
doi:10.35940/ijitee.i7076.079920 fatcat:mjnswo4gxrfb3brwepg25j3ztq

Mental Illness Detection Through Audio Signal Processing

Pravin Karmore
2020 Bioscience Biotechnology Research Communications  
These segments are further pushed into deep networks for feature extraction using neural networks.  ...  From the fragmented audio segments, Mel frequency cepstral constant (MFCC) and combined audio features are extracted using deep neural networks.  ... 
doi:10.21786/bbrc/13.14/71 fatcat:bxq3cr7ypfevtk4wyy2s47o54q

Bag-of-Acoustic-Words for Mental Health Assessment: A Deep Autoencoding Approach

Wenchao Du, Louis-Philippe Morency, Jeffrey Cohn, Alan W. Black
2019 Interspeech 2019  
Despite the recent success of deep learning, it is generally difficult to apply end-to-end deep neural networks to small datasets, such as those from the health domain, due to the tendency of neural networks  ...  are learned jointly by a deep autoencoder, then the bagof-words representation of speech is used for classification, using classifiers with simple decision boundaries.  ...  In our approach, segmentation of audio signals is realized through the convolutional neural network.  ... 
doi:10.21437/interspeech.2019-3059 dblp:conf/interspeech/DuMCB19 fatcat:aymtrthv5zhelpj47plk2cjz6e

Towards Computer-Based Automated Screening of Dementia Through Spontaneous Speech

Karol Chlasta, Krzysztof Wołk
2021 Frontiers in Psychology  
We also proposed (2) DemCNN, a new PyTorch raw waveform-based convolutional neural network model that was 63.6% accurate, 7% more accurate then the best-performing baseline linear discriminant analysis  ...  This paper concerns using Spontaneous Speech (ADReSS) Challenge of Interspeech 2020 to classify Alzheimer's dementia.  ...  FIGURE 2 | 2 Architecture diagram of DemCNN, a custom PyTorch convolutional neural network for speech classification.  ... 
doi:10.3389/fpsyg.2020.623237 pmid:33643116 pmcid:PMC7907518 fatcat:msp5aw5vyzevhgod7veznmd56u

Automated speech-based screening of depression using deep convolutional neural networks [article]

Karol Chlasta, Krzysztof Wołk, Izabela Krejtz
2019 arXiv   pre-print
This paper proposes a novel approach to automated depression detection in speech using convolutional neural network (CNN) and multipart interactive training.  ...  The model was tested using 2568 voice samples obtained from 77 non-depressed and 30 depressed individuals.  ...  This paper proposes a novel method for automated speech-based screening of depression using deep convolutional neural networks.  ... 
arXiv:1912.01115v1 fatcat:ocncs62pjnbhlld4su2hzk2fgi

2020 Index IEEE Transactions on Affective Computing Vol. 11

2021 IEEE Transactions on Affective Computing  
-that appeared in this periodical during 2020, and items from previous years that were commented upon or corrected in 2020.  ...  Using Convolutional Neural Networks.  ...  Facial Video Using Convolutional Neural Networks; T-AFFC Oct.  ... 
doi:10.1109/taffc.2021.3055662 fatcat:het65admgnbbvn4fdzdgmftuqu

IEEE Access Special Section Editorial: Emerging Deep Learning Theories and Methods for Biomedical Engineering

Yu-Dong Zhang, Zhengchao Dong, Juan Manuel Gorriz, Yizhang Jiang, Ming Yang, Shui-Hua Wang
2021 IEEE Access  
She has served as a Visiting Scholar for the Department of Radiology, University of Maryland from January 2013 to June 2014.  ...  ., master's, and doctor's degrees in medical imaging from Southeast University, Nanjing, China, in 1995China, in , 2004China, in , and 2011.  ...  The article ''Recognition of audio depression based on convolutional neural network and generative antagonism network model,'' by Wang et al., proposes an audio depression recognition method based on convolution  ... 
doi:10.1109/access.2021.3080355 fatcat:oez6u3npt5ff7aw7tscwyvlmvq

Emotion Based Music Player

Vinayak Bali, Shubham Haval, Snehal Patil, R. Priyambiga
2019 Zenodo  
Numerous algorithms have been implemented to automate this process. However, existing algorithms are slow, increase cost of the system by using additional hardware and have quite very less accuracy.  ...  The most important goal is to make change the mood of person if it is a negative one such as sad, depressed.  ...  Fasel, -Robust face analysis using convolutional neural networks,‖ in 16th International Conference on Pattern Recognition, 2002. Proceed-ings, vol. 2, 2002, pp. 40-43 vol.2. XVIII.  ... 
doi:10.5281/zenodo.2647696 fatcat:lltimslmlfcotepngakml7p7b4

Sportsman's Mental State Evaluation and Early Warning Method Based on Intelligent CNN

Suxuan Xing, Rahman Ali
2022 Scientific Programming  
This approach uses the text of student forums within universities as the database and introduces the convolutional neural network (CNN) model in deep learning, which contains a convolutional layer, a pooling  ...  After the convolution is completed, the convolution result is delinearized by the activation function, and then, pooling is performed to improve the fitting ability of the network for nonlinearities.  ...  in this paper is a convolutional neural network (CNN) in deep learning.  ... 
doi:10.1155/2022/4711490 fatcat:lgedhhamtvejhfxvgv7eizhske

Construction and Drug Evaluation Based on Convolutional Neural Network System Optimized by Grey Correlation Analysis

Hui Teng, Syed Hassan Ahmed
2021 Computational Intelligence and Neuroscience  
In this article, the grey correlation analysis of patient data is carried out, and then, the optimized deep convolution neural network is constructed.  ...  The purpose of this study is to develop and construct a grey correlation analysis and related drug evaluation system of mental diseases based on deep convolution neural network.  ...  Based on the above advantages, deep convolution neural network is widely used in many industries, including but not limited to face, image, and speech recognition [6] [7] [8] .  ... 
doi:10.1155/2021/2794588 pmid:34567098 pmcid:PMC8460368 fatcat:andyevwktnbmtmlu2cxxbcwh3y

Depression Detection using speech as Input Signal

Aniket Waghela, Prinkle Singharia, Bhavya Haria, Bhakti Sonawane
2020 Zenodo  
Convolutional Neural Network (CNN) classifier is used to find patterns in audio characteristics of the depressed patients.  ...  This training is carried out on DAIC-WOZ Dataset from USC's Institute of Creative Technologies and it was released as part of the AVEC(Audio/Visual Emotional Challenge) 2016.  ...  Convolutional Neural Network(CNN). Many breakthroughs have been achieved by CNN in the field of image processing and is being used widely for signal processing and speech recognition.  ... 
doi:10.5281/zenodo.4166324 fatcat:su3l2z63bjgbfeo6lfszfqg2jm

Introduction to the Issue on Automatic Assessment of Health Disorders Based on Voice, Speech, and Language Processing

Juan I. Godino-Llorente, Douglas O'Shaughnessy, Tan Lee, Najim Dehak, Claudia Manfredi
2020 IEEE Journal on Selected Topics in Signal Processing  
extracted from breathing segments within continuous speech signals, information acquired from sustained vowels using a convolutional neural network, and inherent information in continuous speech signals  ...  The authors investigate the use of spectro-temporal representations to evaluate intelligibility levels using artificial neural network (ANN) and convolutional neural network (CNN), concluding that the  ...  His recent research interests include automatic assessment of voice disorder, speech disorder and language impairment, analysis of child and elderly speech, and speech under pressure.  ... 
doi:10.1109/jstsp.2020.2978566 fatcat:32x76k4fnfhpbcpixz365muapm

Hybrid Network Feature Extraction for Depression Assessment from Speech

Ziping Zhao, Qifei Li, Nicholas Cummins, Bin Liu, Haishuai Wang, Jianhua Tao, Björn W. Schuller
2020 Interspeech 2020  
The proposed network leverages self-attention networks (SAN) trained on low-level acoustic features and deep convolutional neural networks (DCNN) trained on 3D Log-Mel spectrograms.  ...  One vital challenge in the development of speech-based depression severity assessment systems is the extraction of depression-relevant features from speech signals.  ...  when conducting depression analysis from speech.  ... 
doi:10.21437/interspeech.2020-2396 dblp:conf/interspeech/ZhaoLCLWTS20 fatcat:tp3tsfejfvdelodrmbu4xb7h3y
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