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Found 16 papers, 2 papers with code
Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model
Our study identified 29 topics in 3919 smartphone-collected speech recordings from 265 participants using the Whisper tool and BERTopic model.
Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis
Furthermore, we stratified the participants based on their behavioral difference, quantified by the features, between periods of high (depression) and low (no depression) PHQ-8 scores using the Gaussian mixture model.
Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data
Predicting the presence of major depressive disorder (MDD) using behavioural and cognitive signals is a highly non-trivial task.
Detecting the Severity of Major Depressive Disorder from Speech: A Novel HARD-Training Methodology
In this regard, speech samples were collected as part of the Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD) research programme.
Speech and the n-Back task as a lens into depression. How combining both may allow us to isolate different core symptoms of depression
Finally, we present a set of experiments that highlight the association between different speech and n-Back markers at the PHQ-8 item level.
Automatic Detection of Expressed Emotion from Five-Minute Speech Samples: Challenges and Opportunities
We present a novel feasibility study on the automatic recognition of Expressed Emotion (EE), a family environment concept based on caregivers speaking freely about their relative/family member.
Associations between depression symptom severity and daily-life gait characteristics derived from long-term acceleration signals in real-world settings
The gait cadence of faster steps (75th percentile) over a long-term period has a significant negative association with the depression symptom severity of this period in both datasets.
The utility of wearable devices in assessing ambulatory impairments of people with multiple sclerosis in free-living conditions
In this work, we extracted 96 activity features in different temporal granularities (from minute-level to day-level) and explored their utility in estimating 6MWT scores in a European (Italy, Spain, and Denmark) MS cohort of 337 participants over an average of 10-month duration.
Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study
We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features.
Fitbeat: COVID-19 Estimation based on Wristband Heart Rate
This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data.
The Ambiguous World of Emotion Representation
A key reason for this is the lack of a common mathematical framework to describe all the relevant elements of emotion representations.
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition
The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions.
Voice command generation using Progressive Wavegans
Generative Adversarial Networks (GANs) have become exceedingly popular in a wide range of data-driven research fields, due in part to their success in image generation.
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains.
Calibrated Prediction Intervals for Neural Network Regressors
Despite their obvious aforementioned advantage in relation to accuracy, contemporary neural networks can, generally, be regarded as poorly calibrated and as such do not produce reliable output probability estimates.
auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks
auDeep is a Python toolkit for deep unsupervised representation learning from acoustic data.
Sound Audio and Speech Processing
