Acoustic analysis assessment in speech pathology detection

Panek, Daria; Skalski, Andrzej; Gajda, Janusz; Tadeusiewicz, Ryszard

doi:10.1515/amcs-2015-0046

Cited by 41 publications

(25 citation statements)

References 46 publications

(34 reference statements)

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“…In recent years, machine learning methods have been very popular and effective in solving problems in various fields [37][38][39][40][41][42][43][44]. Thus, to classify the extracted features, the MATLAB 2016a classification learner toolbox was used.…”

Section: The Used Definitions For the Proposed Methodsmentioning

confidence: 99%

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

et al. 2019

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In this study, a new face recognition architecture is proposed using fuzzy-based Discrete Wavelet Transform (DWT) and fuzzy with two novel local graph descriptors. These graph descriptors are called Local Cross Pattern (LCP). The proposed fuzzy wavelet-based face recognition architecture consists of DWT, Triangular fuzzy set transformation, and textural feature extraction with local descriptors and classification phases. Firstly, the LL (Low-Low) sub-band is obtained by applying the 2 Dimensions Discrete Wavelet Transform (2D DWT) to face images. After that, the triangular fuzzy transformation is applied to this band in order to obtain A, B, and C images. The proposed LCP is then applied to the B image. LCP consists of two types of descriptors: Vertical Local Cross Pattern (VLCP) and Horizontal Local Cross Pattern (HLCP). Linear discriminant analysis, quadratic discriminant, analysis, quadratic kernel-based support vector machine (QKSVM), and K-nearest neighbors (KNN) were ultimately used to classify the extracted features. Ten widely used descriptors in the literature are applied to the fuzzy wavelet architecture. AT&T, CIE, Face94, and FERET databases are used for performance evaluation of the proposed methods. Experimental results show that the LCP descriptors have high face recognition performance, and the fuzzy wavelet-based model significantly improves the performances of the textural descriptors-based face recognition methods. Moreover, the proposed fuzzy-based domain and LCP method achieved classification accuracy rates of 97.3%, 100.0%, 100.0%, and 96.3% for AT&T, CIE, Face94, and FERET datasets, respectively.

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Section: The Used Definitions For the Proposed Methodsmentioning

confidence: 99%

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

et al. 2019

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“…To compare linear reduction method, 2 non-linear transformation methods were used: non-linear PCA (NLPCA) based on neural networks and kernel PCA (kPCA) with the usage of Gauss kernel function. PCA, kPCA and NLPCA methods were already described in our previous work [1].…”

Section: Methodsmentioning

confidence: 99%

“…There is still a challenge in finding new features that could discriminate between normal and pathological voices or even assess their quality, but also find different approaches on classification [1]- [6]. Voice pathology detection by the usage of acoustic signal processing can be measured, described qualitatively and quantitatively.…”

Section: Introductionmentioning

confidence: 99%

Voice pathology detection by fuzzy logic

Panek

Skalski

Gajda

2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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In this paper an efficient feature extraction methods and fuzzy logic based disorder assessment technique were used to investigate voice signals of patients suffering from functional dysphonia, hyperfunctional dysphonia, vocal cord paralysis and laryngitis. In this work, a vector made up from 28 acoustic parameters was an input for Principal Component Analysis, kernel Principal Component Analysis and Auto-associative Neural Network. Using S-shaped membership function of fuzzy logic, signals were clustered into 2 classes -healthy and pathology one. The amount of fuzzy membership of normal and pathological voice signals in their corresponding clusters was a measure to quantify the membership of the features of a particular class. In the end, S-shaped fuzzy logic method was used as a way of voice pathology detection. A classification accuracy up to 100 percent was achieved using initial 28 feature vector.This full text paper was peer-reviewed at the direction of IEEE Instrumentation and Measurement Society prior to the acceptance and publication.978-1-4799-6144-6/15/$31.00 ©2015 IEEE

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“…Vast majority of studies focus on combining various features without any physiological reasoning. Extensive and summarized reviews on acoustic analysis of pathological voice can be found in Arroyave et al (2012), Vaičiukynas et al (2015), Panek et al (2015).…”

Section: Acoustic Speech Analysis For Voice Disordersmentioning

confidence: 99%

Inverse Filtering of Speech Signal for Detection of Vocal Fold Paralysis After Thyroidectomy

Rybakovas¹,

Beiša²,

Strupas³

et al. 2018

Informatica

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The Autoregressive model-based digital inverse filtering technique is applied in noninvasive detection of vocal fold paralysis. The vocal tract filter is modelled using variable order (up to 20) AR model which is adequate to individual characteristics of human vocal properties. This postulates the more accurate estimation of the glottal flow, disturbances of which are direct evidence of the vocal fold paralysis.

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Acoustic analysis assessment in speech pathology detection

Cited by 41 publications

References 46 publications

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

Voice pathology detection by fuzzy logic

Inverse Filtering of Speech Signal for Detection of Vocal Fold Paralysis After Thyroidectomy

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