Acoustic differences between healthy and depressed people: a cross-situation study

Wang, Jingying; Zhang, Lei; Liu, Tianli; Wei, Pan; Hu, Bin; Zhu, Tingshao

doi:10.1186/s12888-019-2300-7

Cited by 53 publications

(41 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Speech has been demonstrated to have diagnostic validity for Alzheimer's disease (AD) and mild cognitive impairment (MCI) in studies using machine-learning classification models to differentiate individuals with AD/MCI from healthy individuals based on speech samples [34][35][36][37][38][39][40][41]. Additionally, speech analysis has been shown to be able to detect individuals with depression [42][43][44][45], schizophrenia [46][47][48][49], autism spectrum disorder [50], and Parkinson's disease [51,52], and can differentiate the subtypes of primary progressive aphasia and frontotemporal dementia [53][54][55]. Classification models provide diagnostic validity for speech measures and could be used to develop tools for disease screening and diagnosis.…”

Section: Clinical Validationmentioning

confidence: 99%

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

Robin¹,

Harrison

Kaufman³

et al. 2020

Digit Biomark

View full text Add to dashboard Cite

Speech represents a promising novel biomarker by providing a window into brain health, as shown by its disruption in various neurological and psychiatric diseases. As with many novel digital biomarkers, however, rigorous evaluation is currently lacking and is required for these measures to be used effectively and safely. This paper outlines and provides examples from the literature of evaluation steps for speech-based digital biomarkers, based on the recent V3 framework (Goldsack et al., 2020). The V3 framework describes 3 components of evaluation for digital biomarkers: verification, analytical validation, and clinical validation. Verification includes assessing the quality of speech recordings and comparing the effects of hardware and recording conditions on the integrity of the recordings. Analytical validation includes checking the accuracy and reliability of data processing and computed measures, including understanding test-retest reliability, demographic variability, and comparing measures to reference standards. Clinical validity involves verifying the correspondence of a measure to clinical outcomes which can include diagnosis, disease progression, or response to treatment. For each of these sections, we provide recommendations for the types of evaluation necessary for speech-based biomarkers and review published examples. The examples in this paper focus on speech-based biomarkers, but they can be used as a template for digital biomarker development more generally.

show abstract

Section: Clinical Validationmentioning

confidence: 99%

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

Robin¹,

Harrison

Kaufman³

et al. 2020

Digit Biomark

View full text Add to dashboard Cite

show abstract

“…As depression has been linked to changes of loudness in past research [30, 55], we calculated associations of depression scores with voice features ( r = 0.24, t =6.6, p < 0.001). However, depression scores were not related to ADHD symptom severity, thus we did not include it as covariate in the main analysis.…”

Section: Resultsmentioning

confidence: 99%

“…The prediction of schizophrenia has been successful using linguistic features such as semantic relatedness of individuals with schizophrenia or those at a high risk to develop acute symptoms [42, 61-64]. Looking at the studies that have investigated the machine learning based prediction of ADHD from other biological signals points towards a similar performance of approaches based on neuropsychological performance [65], EEG-measures [55, 66], questionnaires [67] or resting state fMRI [68-70] as compared to our findings. In summary, the findings in this study are broadly comparable to previous research using voice to predict mental disorders or other biological signals to predict ADHD.…”

Section: Discussionmentioning

confidence: 99%

Predicting adult Attention Deficit Hyperactivity Disorder (ADHD) using vocal acoustic features

Polier

Ahlers

Amunts

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: It is a key concern in psychiatric research to investigate objective measures to support and ultimately improve diagnostic processes. Current gold standard diagnostic procedures for attention deficit hyperactivity disorder (ADHD) are mainly subjective and prone to bias. Objective measures such as neuropsychological measures and EEG markers show limited specificity. Recent studies point to alterations of voice and speech production to reflect psychiatric symptoms also related to ADHD. However, studies investigating voice in large clinical samples allowing for individual-level prediction of ADHD are lacking. The aim of this study was to explore a role of prosodic voice measures as objective marker of ADHD. Methods: 1005 recordings were analyzed from 387 ADHD patients, 204 healthy controls, and 100 clinical (psychiatric) controls. All participants (age range 18-59 years, mean age 34.4) underwent an extensive diagnostic examination according to gold standard methods and provided speech samples (3 min in total) including free and given speech. Paralinguistic features were calculated, and random forest based classifications were performed using a 10-fold cross-validation with 100 repetitions controlling for age, sex, and education. Association of voice features and ADHD-symptom severity assessed in the clinical interview were analyzed using random forest regressions. Results and Conclusion: ADHD was predicted with AUC = 0.76. The analysis of a non-comorbid sample of ADHD resulted in similar classification performance. Paralinguistic features were associated with ADHD-symptom severity as indicated by random forest regression. In female participants, particularly with age < 32 years, paralinguistic features showed the highest classification performance (AUC = 0.86). Paralinguistic features based on derivatives of loudness and fundamental frequency seem to be promising candidates for further research into vocal acoustic biomarkers of ADHD. Given the relatively good performance in female participants independent of comorbidity, vocal measures may evolve as a clinically supportive option in the complex diagnostic process in this patient group.

show abstract

“…Multiple studies have used voice characteristics as objective markers to understand and differentiate various mental states and psychiatric disorders [ 15 ]. These include investigations of voice in depression that identified many acoustic markers [ 13 , 16 , 17 ]. In another study, researchers were able to classify depressed and healthy speech using deep learning techniques applied to both audio and text features [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Acoustic and language analysis of speech for suicidal ideation among US veterans

et al. 2021

View full text Add to dashboard Cite

Background Screening for suicidal ideation in high-risk groups such as U.S. veterans is crucial for early detection and suicide prevention. Currently, screening is based on clinical interviews or self-report measures. Both approaches rely on subjects to disclose their suicidal thoughts. Innovative approaches are necessary to develop objective and clinically applicable assessments. Speech has been investigated as an objective marker to understand various mental states including suicidal ideation. In this work, we developed a machine learning and natural language processing classifier based on speech markers to screen for suicidal ideation in US veterans. Methodology Veterans submitted 588 narrative audio recordings via a mobile app in a real-life setting. In addition, participants completed self-report psychiatric scales and questionnaires. Recordings were analyzed to extract voice characteristics including prosodic, phonation, and glottal. The audios were also transcribed to extract textual features for linguistic analysis. We evaluated the acoustic and linguistic features using both statistical significance and ensemble feature selection. We also examined the performance of different machine learning algorithms on multiple combinations of features to classify suicidal and non-suicidal audios. Results A combined set of 15 acoustic and linguistic features of speech were identified by the ensemble feature selection. Random Forest classifier, using the selected set of features, correctly identified suicidal ideation in veterans with 86% sensitivity, 70% specificity, and an area under the receiver operating characteristic curve (AUC) of 80%. Conclusions Speech analysis of audios collected from veterans in everyday life settings using smartphones offers a promising approach for suicidal ideation detection. A machine learning classifier may eventually help clinicians identify and monitor high-risk veterans.

show abstract

Acoustic differences between healthy and depressed people: a cross-situation study

Cited by 53 publications

References 44 publications

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations

Predicting adult Attention Deficit Hyperactivity Disorder (ADHD) using vocal acoustic features

Acoustic and language analysis of speech for suicidal ideation among US veterans

Contact Info

Product

Resources

About