Acoustic allometry and vocal learning in mammals.
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ABSTRACT:
SUBMITTER: Garcia M
PROVIDER: S-EPMC7423041 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo detect unilateral vocal fold paralysis (UVFP) from voice recordings using an explainable model of machine learning.Study designCase series - retrospective with a control group.SettingTertiary care laryngology practice between 2009 to 2019.MethodsPatients with confirmed UVFP through endoscopic examination (N=77) and controls with normal voices matched for age and sex (N=77) were included. Two tasks were used to elicit voice samples: reading the Rainbow Passage and sustaining phonation of the vowel "a". The 88 extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) features were extracted as inputs for four machine learning models of differing complexity. SHAP was used to identify important features.ResultsThe median bootstrapped Area Under the Receiver Operating Characteristic Curve (ROC AUC) score ranged from 0.79 to 0.87 depending on model and task. After removing redundant features for explainability, the highest median ROC AUC score was 0.84 using only 13 features for the vowel task and 0.87 using 39 features for the reading task. The most important features included intensity measures, mean MFCC1, mean F1 amplitude and frequency, and shimmer variability depending on model and task.ConclusionUsing the largest dataset studying UVFP to date, we achieve high performance from just a few seconds of voice recordings. Notably, we demonstrate that while similar categories of features related to vocal fold physiology were conserved across models, the models used different combinations of features and still achieved similar effect sizes. Machine learning thus provides a mechanism to detect UVFP and contextualize the accuracy relative to both model architecture and pathophysiology.
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Project description:Speech impairments often emerge as one of the primary indicators of Parkinson's disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification.
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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.
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Project description:PurposeThis exploratory study aims to investigate variations in voice production in the presence of background noise (Lombard effect) in individuals with nonphonotraumatic vocal hyperfunction (NPVH) and individuals with typical voices using acoustic, aerodynamic, and vocal fold vibratory measures of phonatory function.MethodNineteen participants with NPVH and 19 participants with typical voices produced simple vocal tasks in three sequential background conditions: baseline (in quiet), Lombard (in noise), and recovery (5 min after removing the noise). The Lombard condition consisted of speech-shaped noise at 80 dB SPL through audiometric headphones. Acoustic measures from a microphone, glottal aerodynamic parameters estimated from the oral airflow measured with a circumferentially vented pneumotachograph mask, and vocal fold vibratory parameters from high-speed videoendoscopy were analyzed.ResultsDuring the Lombard condition, both groups exhibited a decrease in open quotient and increases in sound pressure level, peak-to-peak glottal airflow, maximum flow declination rate, and subglottal pressure. During the recovery condition, the acoustic and aerodynamic measures of individuals with typical voices returned to those of the baseline condition; however, recovery measures for individuals with NPVH did not return to baseline values.ConclusionsAs expected, individuals with NPVH and participants with typical voices exhibited a Lombard effect in the presence of elevated background noise levels. During the recovery condition, individuals with NPVH did not return to their baseline state, pointing to a persistence of the Lombard effect after noise removal. This behavior could be related to disruptions in laryngeal motor control and may play a role in the etiology of NPVH.Supplemental materialhttps://doi.org/10.23641/asha.20415600.
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