The Association between Depression Severity, Prosody, and Voice Acoustic Features in Women with Depression
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ABSTRACT:
SUBMITTER: Seifpanahi M
PROVIDER: S-EPMC10715859 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundSeveral studies have investigated the acoustic effects of diagnosed anxiety and depression. Anxiety and depression are not characteristics of the typical aging process, but minimal or mild symptoms can appear and evolve with age. However, the knowledge about the association between speech and anxiety or depression is scarce for minimal/mild symptoms, typical of healthy aging. As longevity and aging are still a new phenomenon worldwide, posing also several clinical challenges, it is important to improve our understanding of non-severe mood symptoms' impact on acoustic features across lifetime. The purpose of this study was to determine if variations in acoustic measures of voice are associated with non-severe anxiety or depression symptoms in adult population across lifetime.MethodsTwo different speech tasks (reading vowels in disyllabic words and describing a picture) were produced by 112 individuals aged 35-97. To assess anxiety and depression symptoms, the Hospital Anxiety Depression Scale (HADS) was used. The association between the segmental and suprasegmental acoustic parameters and HADS scores were analyzed using the linear multiple regression technique.ResultsThe number of participants with presence of anxiety or depression symptoms is low (>7: 26.8% and 10.7%, respectively) and non-severe (HADS-A: 5.4 ± 2.9 and HADS-D: 4.2 ± 2.7, respectively). Adults with higher anxiety symptoms did not present significant relationships associated with the acoustic parameters studied. Adults with increased depressive symptoms presented higher vowel duration, longer total pause duration and short total speech duration. Finally, age presented a positive and significant effect only for depressive symptoms, showing that older participants tend to have more depressive symptoms.ConclusionsNon-severe depression symptoms can be related to some acoustic parameters and age. Depression symptoms can be explained by acoustic parameters even among individuals without severe symptom levels.
| S-EPMC8031302 | biostudies-literature
Project description:Little is known about the nature or extent of everyday variability in voice quality. This paper describes a series of principal component analyses to explore within- and between-talker acoustic variation and the extent to which they conform to expectations derived from current models of voice perception. Based on studies of faces and cognitive models of speaker recognition, the authors hypothesized that a few measures would be important across speakers, but that much of within-speaker variability would be idiosyncratic. Analyses used multiple sentence productions from 50 female and 50 male speakers of English, recorded over three days. Twenty-six acoustic variables from a psychoacoustic model of voice quality were measured every 5 ms on vowels and approximants. Across speakers the balance between higher harmonic amplitudes and inharmonic energy in the voice accounted for the most variance (females = 20%, males = 22%). Formant frequencies and their variability accounted for an additional 12% of variance across speakers. Remaining variance appeared largely idiosyncratic, suggesting that the speaker-specific voice space is different for different people. Results further showed that voice spaces for individuals and for the population of talkers have very similar acoustic structures. Implications for prototype models of voice perception and recognition are discussed.
| S-EPMC6909978 | biostudies-literature
Project description:ObjectiveMajor depressive disorder (MDD) and bipolar disorder (BD) are serious chronic disabling mental and emotional disorders, with symptoms that often manifest atypically in children and adolescents, making diagnosis difficult without objective physiological indicators. Therefore, we aimed to objectively identify MDD and BD in children and adolescents by exploring their voiceprint features.MethodsThis study included a total of 150 participants, with 50 MDD patients, 50 BD patients, and 50 healthy controls aged between 6 and 16 years. After collecting voiceprint data, chi-square test was used to screen and extract voiceprint features specific to emotional disorders in children and adolescents. Then, selected characteristic voiceprint features were used to establish training and testing datasets with the ratio of 7:3. The performances of various machine learning and deep learning algorithms were compared using the training dataset, and the optimal algorithm was selected to classify the testing dataset and calculate the sensitivity, specificity, accuracy, and ROC curve.ResultsThe three groups showed differences in clustering centers for various voice features such as root mean square energy, power spectral slope, low-frequency percentile energy level, high-frequency spectral slope, spectral harmonic gain, and audio signal energy level. The model of linear SVM showed the best performance in the training dataset, achieving a total accuracy of 95.6% in classifying the three groups in the testing dataset, with sensitivity of 93.3% for MDD, 100% for BD, specificity of 93.3%, AUC of 1 for BD, and AUC of 0.967 for MDD.ConclusionBy exploring the characteristics of voice features in children and adolescents, machine learning can effectively differentiate between MDD and BD in a population, and voice features hold promise as an objective physiological indicator for the auxiliary diagnosis of mood disorder in clinical practice.
| S-EPMC10826007 | biostudies-literature
Project description:BackgroundThere are no reliable and validated objective biomarkers for the assessment of depression severity. We aimed to investigate the association between depression severity and timing-related speech features using speech recognition technology.MethodPatients with major depressive disorder (MDD), those with bipolar disorder (BP), and healthy controls (HC) were asked to engage in a non-structured interview with research psychologists. Using automated speech recognition technology, we measured three timing-related speech features: speech rate, pause time, and response time. The severity of depression was assessed using the Hamilton Depression Rating Scale 17-item version (HAMD-17). We conducted the current study to answer the following questions: 1) Are there differences in speech features among MDD, BP, and HC? 2) Do speech features correlate with depression severity? 3) Do changes in speech features correlate with within-subject changes in depression severity?ResultsWe collected 1058 data sets from 241 individuals for the study (97 MDD, 68 BP, and 76 HC). There were significant differences in speech features among groups; depressed patients showed slower speech rate, longer pause time, and longer response time than HC. All timing-related speech features showed significant associations with HAMD-17 total scores. Longitudinal changes in speech rate correlated with changes in HAMD-17 total scores.ConclusionsDepressed individuals showed longer response time, longer pause time, and slower speech rate than healthy individuals, all of which were suggestive of psychomotor retardation. Our study suggests that speech features could be used as objective biomarkers for the assessment of depression severity.
| S-EPMC7485753 | biostudies-literature
Project description:IntroductionAlthough brain magnetic resonance imaging (MRI) is a valuable tool for investigating structural changes in the brain associated with neurodegeneration, the development of non-invasive and cost-effective alternative methods for detecting early cognitive impairment is crucial. The human voice has been increasingly used as an indicator for effectively detecting cognitive disorders, but it remains unclear whether acoustic features are associated with structural neuroimaging.MethodsThis study aims to investigate the association between acoustic features and brain volume and compare the predictive power of each for mild cognitive impairment (MCI) in a large community-based population. The study included participants from the Framingham Heart Study (FHS) who had at least one voice recording and an MRI scan. Sixty-five acoustic features were extracted with the OpenSMILE software (v2.1.3) from each voice recording. Nine MRI measures were derived according to the FHS MRI protocol. We examined the associations between acoustic features and MRI measures using linear regression models adjusted for age, sex, and education. Acoustic composite scores were generated by combining acoustic features significantly associated with MRI measures. The MCI prediction ability of acoustic composite scores and MRI measures were compared by building random forest models and calculating the mean area under the receiver operating characteristic curve (AUC) of 10-fold cross-validation.ResultsThe study included 4,293 participants (age 57 ± 13 years, 53.9% women). During 9.3±3.7 years follow-up, 106 participants were diagnosed with MCI. Seven MRI measures were significantly associated with more than 20 acoustic features after adjusting for multiple testing. The acoustic composite scores can improve the AUC for MCI prediction to 0.794, compared to 0.759 achieved by MRI measures.DiscussionWe found multiple acoustic features were associated with MRI measures, suggesting the potential for using acoustic features as easily accessible digital biomarkers for the early diagnosis of MCI.
| S-EPMC11192548 | biostudies-literature
Project description:Valid, reliable biomarkers of depression severity and treatment response would provide new targets for clinical research. Noticeable differences in speech production between depressed and nondepressed patients have been suggested as a potential biomarker.One hundred five adults with major depression were recruited into a 4-week, randomized, double-blind, placebo-controlled research methodology study. An exploratory objective of the study was to evaluate the generalizability and repeatability of prior study results indicating vocal acoustic properties in speech may serve as biomarkers for depression severity and response to treatment. Speech samples, collected at baseline and study end point using an automated telephone system, were analyzed as a function of clinician-rated and patient-reported measures of depression severity and treatment response.Regression models of speech pattern changes associated with clinical outcomes in a prior study were found to be reliable and significant predictors of outcome in the current study, despite differences in the methodological design and implementation of the two studies. Results of the current study replicate and support findings from the prior study. Clinical changes in depressive symptoms among patients responding to the treatments provided also reflected significant differences in speech production patterns. Depressed patients who did not improve clinically showed smaller vocal acoustic changes and/or changes that were directionally opposite to treatment responders.This study supports the feasibility and validity of obtaining clinically important, biologically based vocal acoustic measures of depression severity and treatment response using an automated telephone system.
| S-EPMC3409931 | biostudies-literature
Project description:Emotionally relevant stimuli and in particular anger are, due to their evolutionary relevance, often processed automatically and able to modulate attention independent of conscious access. Here, we tested whether attention allocation is enhanced when auditory stimuli are uttered by an angry voice. We recorded EEG and presented healthy individuals with a passive condition where unfamiliar names as well as the subject's own name were spoken both with an angry and neutral prosody. The active condition instead, required participants to actively count one of the presented (angry) names. Results revealed that in the passive condition the angry prosody only elicited slightly stronger delta synchronization as compared to a neutral voice. In the active condition the attended (angry) target was related to enhanced delta/theta synchronization as well as alpha desynchronization suggesting enhanced allocation of attention and utilization of working memory resources. Altogether, the current results are in line with previous findings and highlight that attention orientation can be systematically related to specific oscillatory brain responses. Potential applications include assessment of non-communicative clinical groups such as post-comatose patients.
| S-EPMC4956258 | biostudies-literature
Project description:Aim: Our study is aimed at exploring the correlation between consumption of dietary fiber and the severity of depression symptoms. Methods: This study utilized data from the National Health and Nutrition Examination Survey spanning from 2007 to 2018, employing a cross-sectional design. The relationship between the severity of depression symptoms and intake of total cereals, vegetables, and fruits dietary fiber was assessed using both univariate and multivariate linear/logistic regression analyses. Stratified analyses were conducted based on hypertension, diabetes, dyslipidemia, cancer or malignancy, and cardiovascular disease. Results: This study included 28,852 participants who were classified into 21,696 with nondepression symptoms, 4614 with mild depression symptoms, 1583 with moderate depression symptoms, 684 with moderately severe depression symptoms, and 275 with severe depression symptoms. After adjusting all confounding factors, we observed a negative correlation between total dietary fiber and depression symptoms (beta = -0.004, 95% confidence intervals [CIs]: -0.006, -0.002). Taking nondepression symptoms as a reference, total dietary fiber was found to have an inverse association with moderate (OR = 0.976, 95% CI: 0.962-0.991), moderately severe (OR = 0.963, 95% CI: 0.938-0.990), and severe depression symptoms (OR = 0.960, 95% CI: 0.921-1.001; marginal significance), respectively. Conclusion: The intakes of total dietary fibers might be related to moderate/moderately severe/severe depression symptoms, and a negative association was shown between total dietary fiber intakes and the risk of depression symptoms.
| S-EPMC11535533 | biostudies-literature
Project description:We have previously shown increased depression and anxiety scores in postmenopausal overweight women, when compared to overweight premenopausal women. The mechanisms responsible for these alterations are not understood. Although ghrelin involvement in mood modulation has been suggested, its role is still ambiguous and has not been evaluated in postmenopause. Here we investigated the association of ghrelin with depression and anxiety symptoms in postmenopausal women. Fifty-five postmenopausal women with depression symptoms, who were not in use of hormones or antidepressants, were included in the study. Depression symptoms were evaluated by Beck's Depression Inventory (BDI) and Patient Health Questionnaire-9 (PHQ-9) and anxiety symptoms were evaluated by Beck's Anxiety Inventory (BAI). Women were allocated into three groups, according to BDI classification of mild, moderate, or severe depression symptoms. Anthropometric, biochemical and hormonal parameters were analyzed. Total and acylated ghrelin levels were higher in the severe depression than in the mild depression group. Multivariate regression analyses showed positive associations of BDI scores with acylated ghrelin and BMI, and of PHQ-9 scores with acylated ghrelin and homeostasis model assessment of insulin resistance (HOMA-IR). BAI scores associated positively with waist-to-hip ratio. To the best of our knowledge, this is the first demonstration of an association between acylated ghrelin and the severity of depression symptoms in postmenopausal women. This association may reflect either a physiological response aimed at fighting against depression symptoms or a causal factor of this mental disorder.
| S-EPMC7935977 | biostudies-literature
Project description:BackgroundWith the aging global population and the rising burden of Alzheimer disease and related dementias (ADRDs), there is a growing focus on identifying mild cognitive impairment (MCI) to enable timely interventions that could potentially slow down the onset of clinical dementia. The production of speech by an individual is a cognitively complex task that engages various cognitive domains. The ease of audio data collection highlights the potential cost-effectiveness and noninvasive nature of using human speech as a tool for cognitive assessment.ObjectiveThis study aimed to construct a machine learning pipeline that incorporates speaker diarization, feature extraction, feature selection, and classification to identify a set of acoustic features derived from voice recordings that exhibit strong MCI detection capability.MethodsThe study included 100 MCI cases and 100 cognitively normal controls matched for age, sex, and education from the Framingham Heart Study. Participants' spoken responses on neuropsychological tests were recorded, and the recorded audio was processed to identify segments of each participant's voice from recordings that included voices of both testers and participants. A comprehensive set of 6385 acoustic features was then extracted from these voice segments using OpenSMILE and Praat software. Subsequently, a random forest model was constructed to classify cognitive status using the features that exhibited significant differences between the MCI and cognitively normal groups. The MCI detection performance of various audio lengths was further examined.ResultsAn optimal subset of 29 features was identified that resulted in an area under the receiver operating characteristic curve of 0.87, with a 95% CI of 0.81-0.94. The most important acoustic feature for MCI classification was the number of filled pauses (importance score=0.09, P=3.10E-08). There was no substantial difference in the performance of the model trained on the acoustic features derived from different lengths of voice recordings.ConclusionsThis study showcases the potential of monitoring changes to nonsemantic and acoustic features of speech as a way of early ADRD detection and motivates future opportunities for using human speech as a measure of brain health.
| S-EPMC11377909 | biostudies-literature