ArticleTranslational psychiatry2023
Relative importance of speech and voice features in the classification of schizophrenia and depression.
Article in Translational psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Role of Voice Acoustics in Depression Assessment: Findings From Bibliometric Analysis, Literature Review, and Meta-Analysis.Depression and anxiety · 2026Pooled it
- Diagnostic accuracy of traditional and deep learning methods for detecting depression based on speech features: a systematic review and meta-analysis.BMC psychiatry · 2025Pooled it
- Performance of Automatic Speech Analysis in Detecting Depression: Systematic Review and Meta-Analysis.JMIR mental health · 2025Pooled it
- A Clinical AI-Based System (MoodMon) for Affective Disorders: Algorithm Development and Validation.JMIR AI · 2026Article
- Validating objective and scalable speech markers of depression across two independent psychiatric cohorts.Annals of general psychiatry · 2026Article
- Affective Dimensions in Maternal Voice During Child Feeding in Mothers With and Without Eating Disorder History-Findings From a Machine Learning Analysis of Speech Data.European eating disorders review : the journal of the Eating Disorders Association · 2026Article
- Expressive pragmatic language in mood and psychotic disorders: a systematic review and meta-analysis.Schizophrenia (Heidelberg, Germany) · 2026Review
- Article
- Recognizing anxiety and depression in cancer patients based on speech and facial expressions.Frontiers in psychiatry · 2026Article
- Mood states recognition based on Mandarin speech and deep learning in patients with bipolar disorder.BMC psychiatry · 2025Article
- From Screening to Precision: Searching for Voice Disorder-Specific Acoustic and Auditory-Perceptual Metrics.Journal of voice : official journal of the Voice Foundation · 2025Article
- Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.Translational psychiatry · 2025Article
- Functional Connectivity Changes in Primary Motor Cortex Subregions of Patients With Obstructive Sleep Apnea.Brain and behavior · 2025Article
- Hearing vocals to recognize schizophrenia: speech discriminant analysis with fusion of emotions and features based on deep learning.BMC psychiatry · 2025Article
- The Voice of Endo: Leveraging Speech for an Intelligent System That Can Forecast Illness Flare-ups.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2025Article
- Screening Voice Disorders: Acoustic Voice Quality Index, Cepstral Peak Prominence, and Machine Learning.Folia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP) · 2025Article
- Associations of Vocal Features, Psychiatric Symptoms, and Cognitive Functions in Schizophrenia.Neuropsychiatric disease and treatment · 2025Article
- Measurement of schizophrenia symptoms through speech analysis from PANSS interview recordings.Frontiers in psychiatry · 2025Article
- Taking a look at your speech: identifying diagnostic status and negative symptoms of psychosis using convolutional neural networks.NPP - digital psychiatry and neuroscience · 2025Article
- An adaptive multi-graph neural network with multimodal feature fusion learning for MDD detection.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
15 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Speech is a promising biomarker for schizophrenia spectrum disorder (SSD) and major depressive disorder (MDD). This proof of principle study investigates previously studied speech acoustics in combination with a novel application of voice pathology features as objective and reproducible classifiers for depression, schizophrenia, and healthy controls (HC). Speech and voice features for classification were calculated from recordings of picture descriptions from 240 speech samples (20 participants with SSD, 20 with MDD, and 20 HC each with 4 samples). Binary classification support vector machine (SVM) models classified the disorder groups and HC. For each feature, the permutation feature importance was calculated, and the top 25% most important features were used to compare differences between the disorder groups and HC including correlations between the important features and symptom severity scores. Multiple kernels for SVM were tested and the pairwise models with the best performing kernel (3-degree polynomial) were highly accurate for each classification: 0.947 for HC vs. SSD, 0.920 for HC vs. MDD, and 0.932 for SSD vs. MDD. The relatively most important features were measures of articulation coordination, number of pauses per minute, and speech variability. There were moderate correlations between important features and positive symptoms for SSD. The important features suggest that speech characteristics relating to psychomotor slowing, alogia, and flat affect differ between HC, SSD, and MDD.
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