Stress Classification based on Speech Analysis of MFCC Feature via Machine Learning

2021 ◽  
Author(s):  
Muhammad Syazani Hafiy Hilmy ◽  
Ani Liza Asnawi ◽  
Ahmad Zamani Jusoh ◽  
Khaizuran Abdullah ◽  
Siti Noorjannah Ibrahim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Speech Analysis ◽  
Stress Classification

A Lightweight Machine Learning Approach to Detect Depression from Speech Analysis

2021 ◽  
Author(s):  
Laura Verde ◽  
Gennaro Raimo ◽  
Federica Vitale ◽  
Bruno Carbonaro ◽  
Gennaro Cordasco ◽  
...  
Keyword(s):  
Machine Learning ◽  
Speech Analysis ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Stress Classification of ECG-Derived HRV Features Extracted from Wearable Devices

2021 ◽  
Author(s):  
Kayisan Mary Dalmeida ◽  
Giovanni Luca Masala
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Wearable Devices ◽  
Mental Wellbeing ◽  
Gradient Boosting ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Automobile Crashes ◽  
Machine Learning Model ◽  
Stress Classification

Stress has been identified as one of the major causes of automobile crashes which then lead to high rates of fatalities and injuries each year. Stress can be measured via physiological measurements and in this study the focus will be based on the features that can be extracted by common wearable devices. Hence the study will be mainly focusing on the heart rate variability (HRV). This study is aimed to develop a good predictive model that can accurately classify stress levels from ECG-derived HRV features, obtained from automobile drivers, testing different machine learning methodologies such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF) and Gradient Boosting (GB). Moreover, the models obtained with highest predictive power will be used as reference for the development of a machine learning model that would be used to classify stress from HRV features derived from HRV measurements obtained from wearable devices. We demonstrate that MLP was the ideal stress classifier by achieving a Recall of 80%. The proposed method can be also used on all applications in which is important to monitor the stress level e. g. in physical rehabilitation, anxiety relief or mental wellbeing.

scholarly journals Machine Learning Based Emotion Recognition using Speech Signal

2019 ◽  
Vol 9 (1S5) ◽  
pp. 295-301
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Emotion Recognition ◽  
Speech Signal ◽  
Contemporary Literature ◽  
Speech Analysis ◽  
Speech Emotion Recognition ◽  
Analysis Methods ◽  
Computer Aided

The challenging module in CAS (computer-aided services) has recognized the emotion from the signals of speech. In SER (speech emotion recognition), several schemes have used for extracting emotions from the signals, comprising various classification & speech analysis methods. This manuscript represents an outline of methods & explores some contemporary literature where the existing models have used for emotion recognition based on speech. This literature review presents contributions that made towards emotion recognition of speech and extracted the features for determining emotions.

scholarly journals Depression Detection Through Speech Analysis : A Survey

2019 ◽  
pp. 712-716
Author(s):  
Aastik Malviya ◽  
Rahul Meharkure ◽  
Rohan Narsinghani ◽  
Viraj Sheth ◽  
Pratiksha Meshram
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Speech Analysis ◽  
Medical Illness ◽  
Depression Detection ◽  
The Way

Depression is a common and serious medical illness which affects the way how we think, feel and act. Although harmless in its initial stages, it can cause serious problems if detected at a later stage. Due to advancements in technology, it is now possible to detect signs of depression. Different implementation of machine learning algorithms has been worked upon to detect factors causing depression. It is found that speech of a person is dramatically affected and various vocal features are used to classify depression.

Speech analysis as a tool for detection and monitoring of medical conditions: a review (Preprint)

2020 ◽  
Author(s):  
Mariusz Ziolko
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Voice Quality ◽  
Medical Diagnostics ◽  
Mass Scale ◽  
The State ◽  
Machine Learning Algorithms ◽  
Speech Analysis ◽  
Future Research ◽  
Voice Analysis

BACKGROUND Speech is the main direct means of transmitting information between people. It also carries additional information depending on the state of the speaker: physical features, emotions, psychosocial traits and health conditions. Studying voice features is straightforward, does not require significant investment and can be carried out on a mass scale. However, speech analysis can only be used for initial diagnosis. The outcome of such a pre-diagnosis should indicate the need for standard medical tests. Although speech analysis is currently rarely used in medical diagnostics, it can enable earlier diagnosis and more effective treatment of patients. OBJECTIVE This paper is a systematic review and meta-analysis of recent advancements in using voice analysis for diagnosis and monitoring of some diseases. The goal of this article is to present and compare recent approaches to using speech and voice analysis as biomarkers of diseases. The article takes into account metabolic, respiratory, cardiovascular, endocrine and nervous system disorders. METHODS Articles published between 2010-2019 were selected from PubMed and ISCA Archive, using keywords ‘voice’ and ‘speech’ and respective disorder names. Further selection was performed to identify studies that assessed voice quality quantitatively in selected disorders by acoustic voice analysis (not only perceptual assessment). Information was extracted from each paper in order to compare various aspects of datasets, speech parameters, methods of analysis applied and results obtained. Each chapter starts with a medical description of how each disorder affects voice and contains a summary of different processing approaches, and is supplemented by tables comparing various investigations. Additional diagrams were prepared to illustrate general tendencies and to compare advancements in the state-of-the-art across the analyzed groups of diseases. RESULTS Over 90 research papers were reviewed and over 40 databases were summarized. Basic acoustic parameters which are significantly correlated with each given disorder were developed for cardiovascular, metabolic and endocrine diseases, as well as schizophrenia and amyotrophic lateral sclerosis. The affective and neurodegenerative disorders are well investigated and the majority of papers contain automatic voice recognition and machine learning methods. The main sources of problems were identified and some recommendation for future research were set. CONCLUSIONS Speech analysis is a promising tool for pre-diagnosis of certain disorders. Advanced computerized voice analysis and machine learning algorithms, and the widespread availability of smartphones, means that a diagnosis may be presented during the patient’s appointment with their physician, and even during a telephone conversation.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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