Classification of Normal and Pathological Voice using GA and SVM

Srinivasan, V.; Ramalingam, V.; Sellam, V.

doi:10.5120/9675-4102

Cited by 15 publications

(8 citation statements)

References 10 publications

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“…Due to its good generalization power, SVM is considered a state-of-the-art classifier (Alghowinem et al 2013a), showing great performance in the identification of speech pathologies (Arjmandi and Pooyan 2012;Sellam and Jagadeesan 2014;Wang et al 2011); along with GMM, SVM is the most widely used classification technique using voice parameters (Jiang et al 2017). The greatest performances from different SVM kernels in this dataset support findings from previous studies in the literature.…”

Section: Introductionsupporting

confidence: 77%

Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study

et al. 2020

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Purpose Diagnosis and treatment in psychiatry are still highly dependent on reports from patients and on clinician judgment. This fact makes them prone to memory and subjectivity biases. As for other medical fields, where objective biomarkers are available, there has been an increasing interest in the development of such tools in psychiatry. To this end, vocal acoustic parameters have been recently studied as possible objective biomarkers, instead of otherwise invasive and costly methods. Patients suffering from different mental disorders, such as major depressive disorder (MDD), may present with alterations of speech. These can be described as uninteresting, monotonous, and spiritless speech and low voice. Methods Thirty-three individuals (11 males) over 18 years old were selected, 22 of which being previously diagnosed with MDD and 11 healthy controls. Their speech was recorded in naturalistic settings, during a routine medical evaluation for psychiatric patients, and in different environments for healthy controls. Voices from third parties were removed. The recordings were submitted to a vocal feature extraction algorithm, and to different machine learning classification techniques. Results The results showed that random tree models with 100 trees provided the greatest classification performances. It achieved mean accuracy of 87.5575% ± 1.9490, mean kappa index, sensitivity, and specificity of 0.7508 ± 0.0319, 0.9149 ± 0.0204, and 0.8354 ± 0.0254, respectively, for the detection of MDD. Conclusion The use of machine learning classifiers with vocal acoustic features appears to be very promising for the detection of major depressive disorder in this exploratory study, but further experiments with a larger sample will be necessary to validate our findings.

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Section: Introductionsupporting

confidence: 77%

Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study

et al. 2020

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“…The process of feature selection is to select the best features that describe the speaker when dealing with hundreds of features that lead to increasing the workload of recognition. Selecting the best features set leads to reducing the classifier training time and as well as increasing the classification accuracy [23]. The accuracy of classification using principal component analysis in addition to discrete wavelet + curvelet is shown in Tables III and IV. From Tables III and IV, it is inferred that the accuracy was impacted positively and it is clear that reducing the features by using PCA did not affect the classification accuracy where the classification accuracy of level one and level two was increased to achieve the best classification and the accuracy of level three still 100%.…”

Section: Assessment Of Resultsmentioning

confidence: 99%

Speaker Identification based on Hybrid Feature Extraction Techniques

AbuAladas¹,

Akram²,

Shaban³

et al. 2019

ijacsa

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One of the most exciting areas of signal processing is speech processing; speech contains many features or characteristics that can discriminate the identity of the person. The human voice is considered one of the important biometric characteristics that can be used for person identification. This work is concerned with studying the effect of appropriate extracted features from various levels of discrete wavelet transformation (DWT) and the concatenation of two techniques (discrete wavelet and curvelet transform) and study the effect of reducing the number of features by using principal component analysis (PCA) on speaker identification. Backpropagation (BP) neural network was also introduced as a classifier.

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“…The SVM with radial basis function as a kernel was utilized because it has less restriction on the data volume and number of features, more general than the linear kernels and it produces better accuracy compared to other kernel functions [24,25]. Intuitively, a good separation is achieved by the hyperplane that has the largest distance to the nearest training data point of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier [26]. The best combination of two SVM parameters; cost (c) and gamma (γ) were obtained using LIBSVM selection tool which has been implemented by Chang and Lin [27].…”

Section: Classificationmentioning

confidence: 99%

Optimization of Dual-Tree Complex Wavelet Packet Based Entropy Features for Voice Pathologies Detection

et al. 2020

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The Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) has been successfully implemented in numerous field because it introduces limited redundancy, provides approximately shift-invariance and geometrically oriented signal in multiple dimensions where these properties are lacking in traditional wavelet transform. This paper investigates the performance of features extracted using DT-CWPT algorithms which are quantified using k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) classifiers for detecting voice pathologies. Decomposition is done on the voice signals using Shannon and Approximate entropy (ApEn) to signify the complexity of voice signals in time and frequency domain. Feature selection methods using the ReliefF algorithm and Genetic algorithm (GA) are applied to obtain the optimum features for multiclass classification. It is observed that the best accuracies obtained using DT-CWPT with ApEn entropy are 91.15 % for k-NN and 93.90 % for SVM classifiers. The proposed work provides a promising detection rate for multiple voice disorders and is useful for the development of computer-based diagnostic tools for voice pathology screening in health care facilities.

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Classification of Normal and Pathological Voice using GA and SVM

Cited by 15 publications

References 10 publications

Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study

Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study

Speaker Identification based on Hybrid Feature Extraction Techniques

Optimization of Dual-Tree Complex Wavelet Packet Based Entropy Features for Voice Pathologies Detection

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