Association between acoustic speech features and non-severe levels of anxiety and depression symptoms across lifespan

Albuquerque, Luciana; Valente, Ana Rita; Teixeira, António; Figueiredo, Daniela; Sá‐Couto, Pedro; Oliveira, Catarina

doi:10.1371/journal.pone.0248842

Cited by 26 publications

(20 citation statements)

References 76 publications

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“…This refers to the amount of speech and related metrics such as the percentage of silence. These features have been shown to be related to anxiety in 3 studies [ 16 , 18 , 20 ]. Our specific feature was the amount of time, in seconds, that speech was present.…”

Section: Methodsmentioning

confidence: 99%

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Acoustic and Linguistic Features of Impromptu Speech and Their Association With Anxiety: Validation Study

Teferra¹,

Borwein²,

DeSouza³

et al. 2022

JMIR Ment Health

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Background The measurement and monitoring of generalized anxiety disorder requires frequent interaction with psychiatrists or psychologists. Access to mental health professionals is often difficult because of high costs or insufficient availability. The ability to assess generalized anxiety disorder passively and at frequent intervals could be a useful complement to conventional treatment and help with relapse monitoring. Prior work suggests that higher anxiety levels are associated with features of human speech. As such, monitoring speech using personal smartphones or other wearable devices may be a means to achieve passive anxiety monitoring. Objective This study aims to validate the association of previously suggested acoustic and linguistic features of speech with anxiety severity. Methods A large number of participants (n=2000) were recruited and participated in a single web-based study session. Participants completed the Generalized Anxiety Disorder 7-item scale assessment and provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test. Acoustic and linguistic speech features were a priori selected based on the existing speech and anxiety literature, along with related features. Associations between speech features and anxiety levels were assessed using age and personal income as covariates. Results Word count and speaking duration were negatively correlated with anxiety scores (r=–0.12; P<.001), indicating that participants with higher anxiety scores spoke less. Several acoustic features were also significantly (P<.05) associated with anxiety, including the mel-frequency cepstral coefficients, linear prediction cepstral coefficients, shimmer, fundamental frequency, and first formant. In contrast to previous literature, second and third formant, jitter, and zero crossing rate for the z score of the power spectral density acoustic features were not significantly associated with anxiety. Linguistic features, including negative-emotion words, were also associated with anxiety (r=0.10; P<.001). In addition, some linguistic relationships were sex dependent. For example, the count of words related to power was positively associated with anxiety in women (r=0.07; P=.03), whereas it was negatively associated with anxiety in men (r=–0.09; P=.01). Conclusions Both acoustic and linguistic speech measures are associated with anxiety scores. The amount of speech, acoustic quality of speech, and gender-specific linguistic characteristics of speech may be useful as part of a system to screen for anxiety, detect relapse, or monitor treatment.

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Section: Methodsmentioning

confidence: 99%

“…Albuquerque et al [ 18 ] investigated the relationship between acoustic speech features and anxiety. They recruited 112 adult Portuguese speakers who performed 2 tasks: reading vowels in disyllabic words and picture description.…”

Section: Introductionmentioning

confidence: 99%

Acoustic and Linguistic Features of Impromptu Speech and Their Association With Anxiety: Validation Study

Teferra¹,

Borwein²,

DeSouza³

et al. 2022

JMIR Ment Health

View full text Add to dashboard Cite

show abstract

“…or in particular, disease progression over time 27 . Third, we exclude PD patients with depression because the existence of depression has been shown to affect the speech features 28 , which would contribute to be a confounding factor in this analysis. However, depression is a common non-motor feature or even a prodromal feature of PD, future studies incorporating PD patients with or without depression would provide a more comprehensive picture to assist the early diagnosis of PD.…”

Section: Discussionmentioning

confidence: 99%

An integrated biometric voice and facial features for early detection of Parkinson’s disease

Lim

Chiu

et al. 2022

npj Parkinsons Dis.

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Hypomimia and voice changes are soft signs preceding classical motor disability in patients with Parkinson’s disease (PD). We aim to investigate whether an analysis of acoustic and facial expressions with machine-learning algorithms assist early identification of patients with PD. We recruited 371 participants, including a training cohort (112 PD patients during “on” phase, 111 controls) and a validation cohort (74 PD patients during “off” phase, 74 controls). All participants underwent a smartphone-based, simultaneous recording of voice and facial expressions, while reading an article. Nine different machine learning classifiers were applied. We observed that integrated facial and voice features could discriminate early-stage PD patients from controls with an area under the receiver operating characteristic (AUROC) diagnostic value of 0.85. In the validation cohort, the optimal diagnostic value (0.90) maintained. We concluded that integrated biometric features of voice and facial expressions could assist the identification of early-stage PD patients from aged controls.

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“…We compared our study to other studies that investigated the approach to analyzing speech in patients affected by depression. Some actual research studies noncontent aspects of speech compared with low or severe depressive symptoms [ 36 ]. The results confirm a relationship between acoustic parameters of speech not only in the case of severe depressive disorder but also in nonsevere cases.…”

Section: Discussionmentioning

confidence: 99%

Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study

Tonn¹,

Seule²,

Degani³

et al. 2022

JMIR Form Res

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Background Mood disorders and depression are pervasive and significant problems worldwide. These represent severe health and emotional impairments for individuals and a considerable economic and social burden. Therefore, fast and reliable diagnosis and appropriate treatment are of great importance. Verbal communication can clarify the speaker’s mental state—regardless of the content, via speech melody, intonation, and so on. In both everyday life and clinical conditions, a listener with appropriate previous knowledge or a trained specialist can grasp helpful knowledge about the speaker's psychological state. Using automated speech analysis for the assessment and tracking of patients with mental health issues opens up the possibility of remote, automatic, and ongoing evaluation when used with patients’ smartphones, as part of the current trends toward the increasing use of digital and mobile health tools. Objective The primary aim of this study is to evaluate the measurements of the presence or absence of depressive mood in participants by comparing the analysis of noncontentual speech parameters with the results of the Patient Health Questionnaire-9. Methods This proof-of-concept study included participants in different affective phases (with and without depression). The inclusion criteria included a neurological or psychiatric diagnosis made by a specialist and fluent use of the German language. The measuring instrument was the VoiceSense digital voice analysis tool, which enables the analysis of 200 specific speech parameters based on machine learning and the assessment of the findings using Patient Health Questionnaire-9. Results A total of 292 psychiatric and voice assessments were performed with 163 participants (males: n=47, 28.8%) aged 15 to 82 years. Of the 163 participants, 87 (53.3%) were not depressed at the time of assessment, and 88 (53.9%) participants had clinically mild to moderate depressive phases. Of the 163 participants, 98 (32.5%) showed subsyndromal symptoms, and 19 (11.7%) participants were severely depressed. In the speech analysis, a clear differentiation between the individual depressive levels, as seen in the Patient Health Questionnaire-9, was also shown, especially the clear differentiation between nondepressed and depressed participants. The study showed a Pearson correlation of 0.41 between clinical assessment and noncontentual speech analysis (P<.001). Conclusions The use of speech analysis shows a high level of accuracy, not only in terms of the general recognition of a clinically relevant depressive state in the participants. Instead, there is a high degree of agreement regarding the extent of depressive impairment with the assessment of experienced clinical practitioners. From our point of view, the application of the noncontentual analysis system in everyday clinical practice makes sense, especially with the idea of a quick and unproblematic assessment of the state of mind, which can even be carried out without personal contact. Trial Registration ClinicalTrials.gov NCT03700008; https://clinicaltrials.gov/ct2/show/NCT03700008

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Association between acoustic speech features and non-severe levels of anxiety and depression symptoms across lifespan

Cited by 26 publications

References 76 publications

Acoustic and Linguistic Features of Impromptu Speech and Their Association With Anxiety: Validation Study

Acoustic and Linguistic Features of Impromptu Speech and Their Association With Anxiety: Validation Study

An integrated biometric voice and facial features for early detection of Parkinson’s disease

Digital Content-Free Speech Analysis Tool to Measure Affective Distress in Mental Health: Evaluation Study

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