ArticleScientific reports2023
Voice disorder classification using convolutional neural network based on deep transfer learning.
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 14 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Detection of Pathological Voice Samples Using a Text-to-Speech-Inspired Approach With Multiple Model Architectures: Method Development and Comparative Evaluation Study.Journal of medical Internet research · 2026Article
- HUPA: A corpus of disordered and normophonic voices in Castilian Spanish.Data in brief · 2026Review
- Post-swallowing voice-based aspiration screening in dysphagia using a deep learning approach: insights from audio segmentation.Scientific reports · 2026Article
- Hybrid deep learning model for multimodal vocal and lung signal analysis in health monitoring.Scientific reports · 2026Article
- Multi-source sparse broad transfer learning for parkinson's disease diagnosis via speech.Medical & biological engineering & computing · 2025Article
- A Deep-Learning Model for Multi-class Audio Classification of Vocal Fold Pathologies in Office Stroboscopy.The Laryngoscope · 2025Article
- Research on automatic assessment of the severity of unilateral vocal cord paralysis based on Mel-spectrogram and convolutional neural networks.Biomedical engineering online · 2025Article
- A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers.BMC medical informatics and decision making · 2025Article
- Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease.Scientific reports · 2025Observational
- Framework for Groove Rating in Exercise-Enhancing Music Based on a CNN-TCN Architecture with Integrated Entropy Regularization and Pooling.Entropy (Basel, Switzerland) · 2025Article
- Analyzing Wav2Vec 1.0 Embeddings for Cross-Database Parkinson's Disease Detection and Speech Features Extraction.Sensors (Basel, Switzerland) · 2024Article
- An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Review
- Classification of laryngeal diseases including laryngeal cancer, benign mucosal disease, and vocal cord paralysis by artificial intelligence using voice analysis.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Voice disorders are very common in the global population. Many researchers have conducted research on the identification and classification of voice disorders based on machine learning. As a data-driven algorithm, machine learning requires a large number of samples for training. However, due to the sensitivity and particularity of medical data, it is difficult to obtain sufficient samples for model learning. To address this challenge, this paper proposes a pretrained OpenL3-SVM transfer learning framework for the automatic recognition of multi-class voice disorders. The framework combines a pre-trained convolutional neural network, OpenL3, and a support vector machine (SVM) classifier. The Mel spectrum of the given voice signal is first extracted and then input into the OpenL3 network to obtain high-level feature embedding. Considering the effects of redundant and negative high-dimensional features, model overfitting easily occurs. Therefore, linear local tangent space alignment (LLTSA) is used for feature dimension reduction. Finally, the obtained dimensionality reduction features are used to train the SVM for voice disorder classification. Fivefold cross-validation is used to verify the classification performance of the OpenL3-SVM. The experimental results show that OpenL3-SVM can effectively classify voice disorders automatically, and its performance exceeds that of the existing methods. With continuous improvements in research, it is expected to be considered as auxiliary diagnostic tool for physicians in the future.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.