A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers.

IF 3.3 3区 医学 Q2 MEDICAL INFORMATICS
Mehtab Ur Rahman, Cem Direkoglu
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引用次数: 0

Abstract

Recent advances in artificial intelligence-based audio and speech processing have increasingly focused on the binary and multi-class classification of voice disorders. Despite progress, achieving high accuracy in multi-class classification remains challenging. This paper proposes a novel hybrid approach using a two-stage framework to enhance voice disorders classification performance, and achieve state-of-the-art accuracies in multi-class classification. Our hybrid approach, combines deep learning features with various powerful classifiers. In the first stage, high-level feature embeddings are extracted from voice data spectrograms using a pre-trained VGGish model. In the second stage, these embeddings are used as input to four different classifiers: Support Vector Machine (SVM), Logistic Regression (LR), Multi-Layer Perceptron (MLP), and an Ensemble Classifier (EC). Experiments are conducted on a subset of the Saarbruecken Voice Database (SVD) for male, female, and combined speakers. For binary classification, VGGish-SVM achieved the highest accuracy for male speakers (82.45% for healthy vs. disordered; 75.45% for hyperfunctional dysphonia vs. vocal fold paresis), while VGGish-EC performed best for female speakers (71.54% for healthy vs. disordered; 68.42% for hyperfunctional dysphonia vs. vocal fold paresis). In multi-class classification, VGGish-SVM outperformed other models, achieving mean accuracies of 77.81% for male speakers, 63.11% for female speakers, and 70.53% for combined genders. We conducted a comparative analysis against related works, including the Mel frequency cepstral coefficient (MFCC), MFCC-glottal features, and features extracted using the wav2vec and HuBERT models with SVM classifier. Results demonstrate that our hybrid approach consistently outperforms these models, especially in multi-class classification tasks. The results show the feasibility of a hybrid framework for voice disorder classification, offering a foundation for refining automated tools that could support clinical assessments with further validation.

一种基于预训练模型和集成分类器的语音障碍二分类和多分类混合方法。
近年来,基于人工智能的音频和语音处理的进展越来越多地集中在语音障碍的二元和多类分类上。尽管取得了进展,但在多类分类中实现高精度仍然具有挑战性。本文提出了一种新的混合方法,采用两阶段框架来提高语音障碍分类性能,并在多类别分类中达到最先进的准确率。我们的混合方法将深度学习特征与各种强大的分类器相结合。在第一阶段,使用预训练的VGGish模型从语音数据谱图中提取高级特征嵌入。在第二阶段,这些嵌入被用作四个不同分类器的输入:支持向量机(SVM)、逻辑回归(LR)、多层感知器(MLP)和集成分类器(EC)。在Saarbruecken语音数据库(SVD)的一个子集上对男性、女性和组合说话人进行了实验。在二值分类中,VGGish-SVM对男性说话者的准确率最高(健康与紊乱的准确率为82.45%;VGGish-EC在女性说话者中表现最佳(健康vs障碍71.54%;68.42%为功能性发声障碍(与声带轻瘫相比)。在多类分类中,VGGish-SVM优于其他模型,对男性说话人的平均准确率为77.81%,对女性说话人的平均准确率为63.11%,对混合性别的平均准确率为70.53%。我们与相关工作进行了对比分析,包括Mel频率倒谱系数(MFCC), MFCC-glottal特征,以及使用SVM分类器使用wav2vec和HuBERT模型提取的特征。结果表明,我们的混合方法始终优于这些模型,特别是在多类分类任务中。结果表明,语音障碍分类混合框架的可行性,为改进自动化工具提供了基础,这些工具可以支持进一步验证的临床评估。
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来源期刊
CiteScore
7.20
自引率
5.70%
发文量
297
审稿时长
1 months
期刊介绍: BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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