ArticleScientific reports2023
A novel hybrid model integrating MFCC and acoustic parameters for voice disorder detection.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Diagnostic Accuracy of Artificial Intelligence in Laryngeal Disorders: An Integrative Review.Journal of personalized medicine · 2026Review
- Noninvasive prescreening of pediatric adenoid hypertrophy using quantified MFCC statistics and clinical features: development and external validation.Frontiers in bioengineering and biotechnology · 2026Article
- Through the Speech and Vocal Signals Hidden Secrets: An Explainable Methodology for Neurological Diseases Early Detection.Journal of healthcare informatics research · 2025Article
- Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease.Scientific reports · 2025Observational
- Pre-trained convolutional neural networks identify Parkinson's disease from spectrogram images of voice samples.Scientific reports · 2025Article
- Wind Turbine Blade Fault Detection Method Based on TROA-SVM.Sensors (Basel, Switzerland) · 2025Article
- TS-Resformer: a model based on multimodal fusion for the classification of music signals.Frontiers in neurorobotics · 2025Article
- Pre-trained Convolutional Neural Networks Identify Parkinson's Disease from Spectrogram Images of Voice Samples.Research square · 2024Article
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Authors and funding
8 authors.
Funding
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Abstract
Voice is an essential component of human communication, serving as a fundamental medium for expressing thoughts, emotions, and ideas. Disruptions in vocal fold vibratory patterns can lead to voice disorders, which can have a profound impact on interpersonal interactions. Early detection of voice disorders is crucial for improving voice health and quality of life. This research proposes a novel methodology called VDDMFS [voice disorder detection using MFCC (Mel-frequency cepstral coefficients), fundamental frequency and spectral centroid] which combines an artificial neural network (ANN) trained on acoustic attributes and a long short-term memory (LSTM) model trained on MFCC attributes. Subsequently, the probabilities generated by both the ANN and LSTM models are stacked and used as input for XGBoost, which detects whether a voice is disordered or not, resulting in more accurate voice disorder detection. This approach achieved promising results, with an accuracy of 95.67%, sensitivity of 95.36%, specificity of 96.49% and f1 score of 96.9%, outperforming existing techniques.
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