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Over the past few decades, the rate of diagnosing depression and mental illness among youths in both genders has been emerging as a challenging issue in the present society. Adequate numbers of cases that have been prevailing had unheard of symptoms linked to mental depression that are able to be detected using their voice recordings and their mess...
Citations
... Among other factors, the speech signal is considered a significant bio-maker in analyzing the mental state. It can be collected from several people in a non-invasive manner with a lack of expert supervision [6]. An adverse scarcity of psychiatrists nationwide pushes people with mental illnesses [7]. ...
Efficient detection of depression is a challenging scenario in the field of speech signal processing. Since the speech signals provide a better diagnosis of depression, a significant methodology is required for detection. However, manual examination performed by radiologists can be time-consuming and may not be feasible in complex circumstances. Diverse detection methodologies have been proposed previously, but they are found to be less accurate, time-consuming and lead over maximized error rates. The proposed research article presents an effective and automatic deep learning-based depression detection using speech signal data. The steps involved in depression prediction are data acquisition, pre-processing, Feature Extraction, Feature selection and classification. The initial step in depression detection is data acquisition, which aims at collecting speech signals from the Distress Analysis Interview Corpus (DAIC-WOZ) and Sonde Health-free speech (SH2-FS) datasets. The collected data are pre-processed through MS_DWT (Multi-stage Discrete Wavelet Transform) to offer noise-free signals and improved signal quality. The relevant features required for processing the speech signal are extracted through Hilbert Huang (H-H) transform linear prediction cepstrum coefficient (LPCC), fundamental frequency, formants, speaking rate and Mel frequency cepstral coefficients (MFCC). From the extracted features, ideal features required for enhancing the detection accuracy are selected using the Price Auction optimization algorithm (PAOA). Finally, the depression and non-depression states are classified using deep convolutional Attention Cascaded two directional long short-term memory (DAttn_Conv 2D LSTM) with a softmax classifier. The overall accuracy obtained in classifying the depressed and non-depressed classes is 97.82% and 98.91%, respectively.
... Finding new methods to determine a disease or predict its progression is a state-of-the-art issue in recent studies, such as using machine learning for Parkinson's disease [1], hepatitis [2], and depression disorder [3,4] or analyzing voice acoustics and prosody to determine depression disorder [5]. ...
... In a study, a signifcantly lower CPP was reported in PWDD compared with the healthy group [19]. Moreover, some fndings indicated that other parameters such as cepstral, spectral, Mel-frequency cepstrum coefcients, and loudness could be considered to screen PWDD from healthy people [3,25,26]. Unlike the other acoustic evaluation methods, CPP is not dependent on pitch tracking to detect the amount of perturbation of the voice signals. Terefore, it could be accounted as one of the best evaluation methods to assess the degree of the vocal harmonics even for the most aperiodic voices [27,28]. ...
The aim was to define the association between the severity of depression, prosody, and voice acoustic features in women suffering from depression and its comparisons with nondepressed people. Prosody and acoustic features in 30 women with major depression hospitalized in a psychiatric ward and 30 healthy women were investigated in a cross-sectional study. To define the severity of depression, the Hamilton Rating Scale for Depression (HRS-D) was applied. Acoustic parameters such as jitter, shimmer, cepstral peak prominence (CPP), standard deviation of fundamental frequency (SD F0), harmonic-to-noise ratio, and F0 and also some speech prosodic features including the speed of speech, switching pause duration means, and durations of produced sentences with different modals were measured quantitatively. Also, six raters judged the patient’s prosody qualitatively. SPSS V.28 was used for all statistical analyses ( p < 0.05 ). There was a significant correlation between HRS-D with jitter, SD F0, speed of speech, and switching pause means ( p ≤ 0.05 ). The means of CPP and duration of producing emotional sentences differed between the depression and control groups. The HRS-D scores were significantly correlated with switching pauses in patients (Pearson coefficient = 0.47, p = 0.05 ). The results of the perceptual evaluation of prosody judged by six raters showed an 85% correlation between them ( p ≤ 0.001 ). Some acoustic and prosodic parameters are different between healthy women and those with depression disorder (e.g., CPP and duration of emotional sentences) and may also have an association with the severity of depression (e.g., jitter, SD F0, speed of speech, and switching pause means) in women with depression disorder. It was indicated that the best sentence modal to assess prosody in patients with depression would be exclamatory ones compared to declarative and interrogative sentences.
... In 2021, a voice biomarker machine learning model for minor and major depression detection was proposed by Shin et al. [15]. In 2022, a deep learning method was proposed by Yue et al. [16] for depressive orders diagnosis, a empirical investigation made by Punithavathi et al. [17] showed machine learning based voice recognition techniques could be used for depression prediction and a GRU/BiLSTM-based model for depression detection was proposed by Shen et al. [18]. However, all aforementioned models were proposed for depression detection but rarely for mania. ...
Background
Both depressive and mania mood state have high prevalence and are important causes of social burden worldwide, however, there is still no objective indicator for detection. This study aimed to examine if voice could be used as a biomarker to detect these symptoms in China.
Methods
1,287 voice messages from 81 subjects were classified into three groups: the depression mood state group (406 voice messages from n = 31), the mania mood state group (192 voice messages from n = 14), and the remission group (689 voice messages from n = 36), based on the scores of the MDQ, QIDS and YMRS. 34 features were extracted from voice records which is collected in real-world emotional diary. A three-group comparison was performed through analysis of Kruskal-Wallis H Test. Three feature extraction methods were adopted and four machine learning methods were performed.
Results
33 voice indicators showed differences among the three groups(p < 0.05). Among the machine learning methods, the best performance was obtained using the Gate Recurrent Unit with 79.6% sensitivity, 91.1% specificity and 82.5% sensitivity, 90.7% specificity for the detection of depressive and mania mood state respectively.
Conclusions
This study further revealed participants with depressive or manic mood state could be accurately distinguished through machine learning. Although this study is limited by a small sample size, it is the first study on voice as a biomarker in both depressive and mania mood state which suggests the possibility of detecting these mood states through voice.
... Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: ...
... Moreover, various mathematicians, psychologists, engineers, medical scientists, computer scientists and many others have invented and sometimes rediscovered some methods of solving problems. Therefore, different methods applicable to emotion prediction in speech recognition were presented in comparative frameworks [2,9]. ...
... Mental disease: Thirty of the reviewed articles dealt with the diagnosis of mental illnesses using speech [1][2][3][7][8][9]11,13,. In fourteen studies, machine learning algorithms were used to diagnose Major Depressive Disorder (MDD) [1,3,8,[31][32][33][34][36][37][38][39][40][41][42]. ...
... Eight of the included studies presented machine learning models for diagnosing anxiety and minor depression [2,7,9,11,[43][44][45][46]. Among these articles, five articles obtained better results, in all of which acoustic sound vignettes were used to diagnose the disease [2,11,43,[45][46]. ...
There is a substantial unmet need to diagnose speech-related disorders effectively. Machine learning (ML), as an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve these issues. The purpose of this study was to categorize and compare machine learning methods in the diagnosis of speech-based diseases. In this systematic review, a comprehensive search for publications was conducted on the Scopus, Web of Science, PubMed, IEEE and Cochrane databases from 2002–2022. From 533 search results, 48 articles were selected based on the eligibility criteria. Our findings suggest that the diagnosing of speech-based diseases using speech signals depends on culture, language and content of speech, gender, age, accent and many other factors. The use of machine-learning models on speech sounds is a promising pathway towards improving speech-based disease diagnosis and treatments in line with preventive and personalized medicine.
Abstract
A subset of machine learning (ML) known as “deep learning” (DL) has gained popularity recently as a result of advancements in the use of artificial neural networks, huge data, and processing power. DL in healthcare is significantly influencing the healthcare system by enhancing diagnosis and raising patient outcome standards. DL helps clinicians analyze data and discover a variety of illnesses, such as heart difficulties and cancers, that can be found using picture analysis, cancer diagnosis using malignant cells found in the body, diabetes patients' blood sugar levels, and cancer that can be found in blood samples. Rapid disease detection made possible by the application of DL and ML has allowed doctors to save lives quickly, spend priceless time with their patients, and decrease hospital stays and healthcare costs.
With the ability to handle “large complex data,” DL and ML have become quite popular over the past 10 years and are now finding application in the healthcare industry. Computational models built on neural networks can learn to describe data at different levels of abstraction, thanks to DL. When ML is used in clinical decision-making, it implies that the system will interpret a particular individual by collecting and analyzing data pertinent to that individual's health, and it will then use the data to explain about the best method that should be used to maintain or improve the individual's health. According to research, ML has the potential to aid in the identification of numerous mental diseases as well as to enhance patient outcomes.
The most quickly expanding concept of the 21st century is the incorporation of AI into medical research. The great potential of AI in healthcare has been revealed by the dramatic rise in AI-related research and publications over the past 10 years. In order to manage the massive and multidimensional data necessary for healthcare research, ML is used. Other applications of AI algorithms in healthcare include diagnosis, prognostication, decision assistance, screening and triage, and treatment suggestion. When used in clinical trials, ML has the potential to improve the process' generalizability, patient-centeredness, accuracy, and success. From preclinical drug development to pretrial planning, including study implementation, to data management and analysis, ML works across the spectrum of clinical trials in healthcare. In actuality, DL machines would not take the job of advisors; instead, they will enhance our abilities to diagnose aberrant lesions in remote settings in a clear and understandable context.
DLM may offer new hope and an affordable alternative for health service providers in low-income countries with diverse, low-density demographics who want to take advantage of technological advancements in developed countries and receive relatively comparable standard care at reasonable costs while investing in expanding their care provision and addressing the shortage of medical professionals needed to reach their goal of universal health coverage.
