Detecting Depression from Voice

Tasnim, Mashrura; Stroulia, Eleni

doi:10.1007/978-3-030-18305-9_47

Cited by 31 publications

(16 citation statements)

References 16 publications

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“…Building on classification guidance from the National Institutes of Health (NIH) and the US Food and Drug Administration (FDA), Coravos et al [ 15 ] have proposed a useful framework of digital biomarkers, which classifies signals as they relate to susceptibility or risk, diagnosis, monitoring, prognostication, and prediction. These audio biomarkers can be used to detect and classify coughs [ 16 , 17 ], discern voice changes arising from neurodegenerative diseases such as Parkinson disease [ 18 ] or dementia [ 19 ], characterize voice changes related to depression [ 20 , 21 ] or other mental illnesses [ 22 ], classify breathing patterns associated with obstructive sleep apnea (OSA) [ 23 ], identify deteriorating asthma [ 24 ], and even identify unwitnessed cardiac arrest by detecting the presence of agonal breathing [ 11 ].…”

Section: Classification Of Medically Relevant Diagnostic Soundsmentioning

confidence: 99%

Smart Speakers: The Next Frontier in mHealth

Sunshine¹

2022

JMIR Mhealth Uhealth

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The rapid dissemination and adoption of smart speakers has enabled substantial opportunities to improve human health. Just as the introduction of the mobile phone led to considerable health innovation, smart speaker computing systems carry several unique advantages that have the potential to catalyze new fields of health research, particularly in out-of-hospital environments. The recent rise and ubiquity of these smart computing systems holds significant potential for enhancing chronic disease management, enabling passive identification of unwitnessed medical emergencies, detecting subtle changes in human behavior and cognition, limiting isolation, and potentially allowing widespread, passive, remote monitoring of respiratory diseases that impact public health. There are 3 broad mechanisms for how a smart speaker can interact with a person to improve health. These include (1) as an intelligent conversational agent, (2) as a passive identifier of medically relevant diagnostic sounds, and (3) by active sensing using the device's internal hardware to measure physiologic parameters, such as with active sonar, radar, or computer vision. Each of these different modalities has specific clinical use cases, all of which need to be balanced against potential privacy concerns, equity concerns related to system access, and regulatory frameworks which have not yet been developed for this unique type of passive data collection.

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Section: Classification Of Medically Relevant Diagnostic Soundsmentioning

confidence: 99%

Smart Speakers: The Next Frontier in mHealth

Sunshine¹

2022

JMIR Mhealth Uhealth

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“…The speech mechanism of an individual is very complex and can be affected by the psychological state of mind. Speech can provide a tremendous amount of help in detecting the mood of any individual [34]. Many experts have done their work in determining the relation between the speech and the depression.…”

Section: Speech Indicatorsmentioning

confidence: 99%

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

Ashraf

Gunawan

Riza³

et al. 2020

IJEECS

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<span>Machine learning has been introduced in the sphere of the medical field to enhance the accuracy, precision, and analysis of diagnostics while reducing laborious jobs. With the mounting evidence, machine learning has the capability to detect mental distress like depression. Since depression is the most prevalent mental disorder in our society at present, and almost the majority of the population suffers from this issue. Hence there is an extreme need for the depression detection models, which will provide a support system and early detection of depression. 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. The indicators of depression are also reviewed in this paper. The evaluation of different researches, along with their performance parameters, is summarized. The paper concludes with remarks about the techniques used and the future scope of using the image and video-based depression prediction. </span>

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“…Berry et al (2017) investigate the popularity of social media as an outlet for mental health discussion at length, finding reasons including anonymity, sense of empowerment, sense of community, and perceptions of the internet as a safe space. A growing number of approaches have sought to leverage social media data to aid in the automated identification of specific MHCs, with work to date including automated detection of depression (Yasaswini et al, 2021;Schwartz et al, 2014a;Tasnim and Stroulia, 2019;Rosenquist et al, 2010), post-traumatic stress disorder (Li et al, 2010), anxiety (Shen and Rudzicz, 2017), and stress (Naik et al, 2018). However, these approaches have lagged behind the state of the art in more fundamental NLP tasks.…”

Section: Introductionmentioning

confidence: 99%

Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses

Aich¹,

Parde²

2022

Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

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Evidence has demonstrated the presence of similarities in language use across people with various mental health conditions. In this work we investigate these relationships both as described in literature and as a data analysis problem. We also introduce a novel transfer learning based approach that learns from linguistic feature spaces of previous conditions and predicts unknown ones. Our model achieves strong performance, with F 1 scores of 0.75, 0.80, and 0.76 at detecting depression, stress, and suicidal ideation in a first-of-its-kind transfer task and offering promising evidence that language models can harness learned patterns from known mental health conditions to aid in their prediction of others that may lie latent.

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Detecting Depression from Voice

Cited by 31 publications

References 16 publications

Smart Speakers: The Next Frontier in mHealth

Smart Speakers: The Next Frontier in mHealth

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

Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses

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