The Reproducibility of Bio-Acoustic Features is Associated With Sample Duration, Speech Task, and Gender

Almaghrabi, Shaykhah A.; Thewlis, Dominic; Thwaites, Simon; Rogasch, Nigel C.; Lau, Stephan; Clark, Scott; Baumert, Mathias

doi:10.1109/tnsre.2022.3143117

Cited by 11 publications

(3 citation statements)

References 59 publications

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“…Scherer et al showed through research that the accuracy of disturbance measurement in the voice was affected by the duration of the voice, and only voice over 3 s could provide accurate features ( 44 ). There are also studies proving that the pitch measurement of long voice is more accurate than that of short voice ( 45 , 46 ). To conclude, in this study, we observed that patients with different emotional states exhibited varying voice durations, indicating that the information carried in their voices differs.…”

Section: Discussionmentioning

confidence: 99%

Depressive and mania mood state detection through voice as a biomarker using machine learning

Ji,

Dong,

et al. 2024

Front. Neurol.

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IntroductionDepressive and manic states contribute significantly to the global social burden, but objective detection tools are still lacking. This study investigates the feasibility of utilizing voice as a biomarker to detect these mood states. Methods:From real-world emotional journal voice recordings, 22 features were retrieved in this study, 21 of which showed significant differences among mood states. Additionally, we applied leave-one-subject-out strategy to train and validate four classification models: Chinese-speech-pretrain-GRU, Gate Recurrent Unit (GRU), Bi-directional Long Short-Term Memory (BiLSTM), and Linear Discriminant Analysis (LDA).ResultsOur results indicated that the Chinese-speech-pretrain-GRU model performed the best, achieving sensitivities of 77.5% and 54.8% and specificities of 86.1% and 90.3% for detecting depressive and manic states, respectively, with an overall accuracy of 80.2%.DiscussionThese findings show that machine learning can reliably differentiate between depressive and manic mood states via voice analysis, allowing for a more objective and precise approach to mood disorder assessment.

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Section: Discussionmentioning

confidence: 99%

Depressive and mania mood state detection through voice as a biomarker using machine learning

Ji,

Dong,

et al. 2024

Front. Neurol.

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“…[ 36 ] A recent study by Almaghrabi et al investigated the reproducibility of bioacoustic features, showing that the stability of such features was easily affected by sample duration, speech task, and gender. [ 37 ] Despite the widespread interest, studies on reliability of automated acoustic measures still remain scarce, especially since most previous research has focused on voice disorders or psychiatric illnesses, and there has been no research on the reliability of acoustic tasks and features specific to dysarthria. To our knowledge, the present study is the first attempt to explore the reproducibility of automated acoustic measures in dysarthria using AI.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence‐Powered Acoustic Analysis System for Dysarthria Severity Assessment

Zhang,

Shang,

Yang

et al. 2023

Advanced Intelligent Systems

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Dysarthria is common in movement disorders, such as Wilson's disease (WD), Parkinson's disease, or Huntington's disease. Dysarthria severity assessment is often indispensable for the management of these diseases. However, such assessment is usually labor‐intensive, time‐consuming, and expensive. To seek efficient and cost‐effective solutions for dysarthria assessment, an artificial intelligence (AI)‐powered acoustic analysis system is proposed and its performance in a valuable sample of WD, an ideal disease model with mainly mixed dysarthria, is verified. A test‐retest reliability analysis yields excellent reproducibility in the acoustic measures (mean intraclass correlation coefficient [ICC] = 0.81). Then, a system for dysarthria assessment is trained with WD patients (n = 65) and sex‐matched healthy controls (n = 65) using a machine learning approach. The system achieves reasonable performance in evaluating dysarthria severity with either stepwise classification or regression (all areas under the curve >80%; mean absolute error = 6.25, r = 0.79, p < 0.0001). The diadochokinesis and sustained phonation tasks contribute the most to prediction, and the corresponding acoustic features can provide significant and independent contributions. The present study demonstrates the feasibility and good performance of the AI‐powered acoustic analysis framework, offering the potential to facilitate early screening and subsequent management of dysarthria.

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“…Artificial intelligence (AI) or machine learning (ML) models that can detect depression severity from speech could be a more practical alternative to increased clinician interviews (Almaghrabi et al, 2023). The pace of AI innovation in processing audiovisual features has led to many proof-of-concept models for mental health detection (Le Glaz et al, 2021) and depression, specifically (Almaghrabi et al, 2023;Cummins et al, 2015;He et al, 2022;Low et al, 2020). These models leverage well-documented changes in speech, affect, and presentation as depression severity increases (Kliper et al, 2016;Zhang et al, 2020).…”

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confidence: 99%

Conversational Assessment Using Artificial Intelligence is as Clinically Useful as Depression Scales and Preferred by Users

Weisenburger,

Mullarkey,

Labrada

et al. 2023

Preprint

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Background: Depression is prevalent, chronic, and burdensome. Due to limited screening access, depression often remains undiagnosed. Artificial intelligence (AI) models based on spoken responses to interview questions may offer an effective, efficient alternative to other screening methods.Objective: The primary aim was to use a demographically diverse sample to validate an AI model, previously trained on human-administered interviews, on novel bot-administered interviews, and to check for algorithmic biases related to age, sex, race, and ethnicity.Methods: Using the Aiberry app, adults recruited via social media (N = 393) completed a brief bot-administered interview and a depression self-report form. An AI model was used to predict form scores based on interview responses alone. For all meaningful discrepancies between model inference and form score, clinicians performed a masked review to determine which one they preferred.Results: There was strong concurrent validity between the model predictions and raw self-report scores (r = 0.73, MAE = 3.3). 90% of AI predictions either agreed with self-report or with clinical expert opinion when AI contradicted self-report. There was no differential model performance across age, sex, race, or ethnicity.Limitations: Limitations include access restrictions (English-speaking ability and access to smartphone or computer with broadband internet) and potential self-selection of participants more favorably predisposed toward AI technology.Conclusion: The Aiberry model made accurate predictions of depression severity based on remotely collected spoken responses to a bot-administered interview. This study shows promising results for the use of AI as a mental health screening tool.

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The Reproducibility of Bio-Acoustic Features is Associated With Sample Duration, Speech Task, and Gender

Cited by 11 publications

References 59 publications

Depressive and mania mood state detection through voice as a biomarker using machine learning

Depressive and mania mood state detection through voice as a biomarker using machine learning

Artificial Intelligence‐Powered Acoustic Analysis System for Dysarthria Severity Assessment

Conversational Assessment Using Artificial Intelligence is as Clinically Useful as Depression Scales and Preferred by Users

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