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Background: Changes in various speech features have been linked to various neurological and mental health-related pathologies; these changes can often be detected years before a definitive clinical diagnosis. With the growing interest in using speech…

Background: Despite the high prevalence and significant burden of #depression, underdiagnosis remains a persistent challenge. Automatic speech analysis (ASA) has emerged as a promising method for #depression assessment. However, a comprehensive quantitative synthesis evaluating its diagnostic accuracy is still lacking. Objective: This systematic review and meta-analysis aimed to assess the diagnostic performance of ASA in detecting #depression, considering both machine learning and deep learning #Approaches. Methods: We conducted a systematic search across 8 databases, including MEDLINE, PsycInfo, Embase, CINAHL, IEEE Xplore, ACM #Digital Library, Scopus, and Google Scholar from January 2013 to April 1, 2025. We included studies published in English that evaluated the accuracy of ASA for detecting #depression, and reported performance metrics such as accuracy, sensitivity, specificity, precision, or confusion matrices. Study quality was assessed using a modified version of the Quality Assessment of Studies of Diagnostic Accuracy-Revised. A 3-level meta-analysis was performed to estimate the pooled highest and lowest accuracy, sensitivity, specificity, and precision. Meta-regressions and subgroup analyses were performed to explore heterogeneity across various factors, including type of publication, artificial intelligence algorithms, speech features, speech-eliciting tasks, ground truth assessment, validation #Approach, dataset, dataset language, participants’ mean age, and sample size. Results: Of the 1345 records identified, 105 studies met the inclusion criteria. The pooled mean of the highest accuracy, sensitivity, specificity, and precision were 0.81 (95% CI 0.79 to 0.83), 0.84 (95% CI 0.81 to 0.86), 0.83 (95% CI 0.79 to 0.86), and 0.81 (95% CI 0.77 to 0.84), respectively, whereas the pooled mean of the lowest accuracy, sensitivity, specificity, and precision were 0.66 (95% CI 0.63 to 0.69), 0.63 (95% CI 0.58 to 0.68), 0.60 (95% CI 0.55 to 0.66), and 0.64 (95% CI 0.58 to 0.70), respectively. Conclusions: ASA shows promise as a method for detecting #depression, though its readiness for clinical #Application as a standalone tool remains limited. At present, it should be regarded as a complementary method, with potential #Applications across diverse contexts. Further high-quality, peer-reviewed studies are needed to support the development of robust, generalizable models and to advance this emerging field. Trial Registration: PROSPERO CRD42023444431; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023444431

A groundbreaking study published in Science Advances has introduced an AI-powered 'speech clock' that assesses how quickly a person is ageing based on vocal characteristics. Developed by researchers at Adolfo Ibáñez University in Santiago, this innovative tool analyzes over 700 speech features including pitch, talking speed, and emotional content to estimate biological age. The research team recorded 2,928 Spanish speakers from multiple Latin American countries while they completed various speech tasks, including individuals with cognitive impairments and dementia. The AI model successfully distinguished between healthy individuals and those with cognitive issues by identifying a 'speech age gap' - the difference between predicted speech age and chronological age. Large speech age gaps were strongly associated with cognitive decline, suggesting the tool could serve as an early indicator for conditions like dementia. Neuroscientist Agustín Ibáñez, a co-author of the study, emphasized the tool's simplicity and effectiveness, noting that just four minutes of speech recordings could provide significant predictive value. The research highlights that speaking involves complex brain activity, making vocal analysis a valuable window into the ageing process. Unlike existing ageing clocks that rely on expensive biological markers like brain scans or blood tests, this speech-based approach offers a non-invasive, cost-effective method for tracking ageing, particularly beneficial for low-resource regions. Cognitive neuroscientist Jed Meltzer from the University of Toronto, who was not involved in the study, praised the work as 'very impressive' and highlighted its potential for broader applications in healthcare monitoring. The study's findings contribute to the growing field of 'ageing clocks' that use various biomarkers to assess biological age, with this particular innovation focusing on the vocal characteristics that change with cognitive decline and ageing. This research opens new possibilities for early detection of age-related cognitive issues through simple, accessible speech analysis.

As part of our ongoing work on the S.O.N.A.R. and VEUS projects—where we leverage automatic detection of prosodic voice features for emotion recognition—Dr. Daniel MARTINEZ URIBE, PhD, collaborator at...

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TikTok video by What's Trending Now News

TikTok video by What's Trending Now News

TikTok video by What's Trending Now News

Why I chose objective acoustic measurements over trying to calculate how feminine a voice sounds.

TikTok video by What's Trending Now News