Voice disorders classification using multilayer neural network

Lotfi, Shahriar; Talbi, Mourad; Cherif, Adnane

doi:10.1109/icscs.2008.4746953

Cited by 15 publications

(18 citation statements)

References 10 publications

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“…Analysis of voice signal is performed by the extraction of acoustic parameters using digital signal processing techniques [6]. However the amount of these parameters is huge to be analyzed, which lead to define the relevant ones.…”

Section: Relevant Acoustic Parametersmentioning

confidence: 99%

Improved Algorithm for Pathological and Normal Voices Identification

Sabir¹,

Rouda²,

Khazri³

et al. 2017

IJECE

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There are a lot of papers on automatic classification between normal and pathological voices, but they have the lack in the degree of severity estimation of the identified voice disorders. Building a model of pathological and normal voices identification, that can also evaluate the degree of severity of the identified voice disorders among students. In the present work, we present an automatic classifier using acoustical measurements on registered sustained vowels /a/ and pattern recognition tools based on neural networks. The training set was done by classifying students' recorded voices based on threshold from the literature. We retrieve the pitch, jitter, shimmer and harmonic-to-noise ratio values of the speech utterance /a/, which constitute the input vector of the neural network. The degree of severity is estimated to evaluate how the parameters are far from the standard values based on the percent of normal and pathological values. In this work, the base data used for testing the proposed algorithm of the neural network is formed by healthy and pathological voices from German database of voice disorders. The performance of the proposed algorithm is evaluated in a term of the accuracy (97.9%), sensitivity (1.6%), and specificity (95.1%). The classification rate is 90% for normal class and 95% for pathological class.

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Section: Relevant Acoustic Parametersmentioning

confidence: 99%

Improved Algorithm for Pathological and Normal Voices Identification

Sabir¹,

Rouda²,

Khazri³

et al. 2017

IJECE

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“…ASR system for Arabic words with various dysphonic patients was studied by Muhammad et al 21 They reported that speech samples from sulcus vocalis patients were recognized the least (56%), whereas that from patients with vocal fold nodules were better recognized (84.5%). Wavelet decomposition and neural network-based voice pathology detection were proposed by Salhi et al 22 Energy from different subbands of wavelet was used as features. One hundred percent accuracy was reported on a very small pathological data set.…”

Section: Etcmentioning

confidence: 99%

Multidirectional Regression (MDR)-Based Features for Automatic Voice Disorder Detection

et al. 2012

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“…What is more, parameters such as pitch analysis, jitter, shimmer, harmonic to noise ratio, mel-frequency cepstral coefficients (MFCC) has been suggested for improving voice pathology detection systems [10], [11]. The classification systems of pathological voices consists of multi-layer perception [12], Gaussian mixture model [13], Support Vector Machine, hidden Markov model [1], probabilistic neural network [14] and linear discriminant analysis [24], [16]. Most of the methods in the literature focus on binary classification to detect whether the voice is classified as normal or pathological, they do not detect the type of voice pathology.…”

Section: Introductionmentioning

confidence: 99%

Voice pathology distinction using autoassociative neural networks

Hemmerling

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Acoustic analysis is a non-invasive technique that supports voice disease screening, especially the detection and diagnosis of distinction between chosen voice pathologies and healthy control group. This work put en effort on creation of efficient and accurate system for automatic detection and differentiation of normal and three different voice pathologies. This system ensures non-invasive and fully automated analysis of voice characteristics and decision system based on neural networks. The feature vector describing the vocal tract is set up from 35 parameters. Recordings of patients suffering from hyperfunctional dysphonia, recurrent laryngeal nerve paralysis, laryngitis and healthy control group are considered in our experiments. From the experimental results it is observed that effectiveness of auto-associative neural networks seems to be promising in the application of pathological voice distinction.

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Voice disorders classification using multilayer neural network

Cited by 15 publications

References 10 publications

Improved Algorithm for Pathological and Normal Voices Identification

Improved Algorithm for Pathological and Normal Voices Identification

Multidirectional Regression (MDR)-Based Features for Automatic Voice Disorder Detection

Voice pathology distinction using autoassociative neural networks

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