Automatic Detection of Pathologies in The Voice by HOS Based Parameters

Alonso, Jesús B.; Leon, José de; Alonso, Itziar G.; Ferrer, Miguel A.

doi:10.1155/s1110865701000336

Cited by 91 publications

(77 citation statements)

References 25 publications

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“…The quadratic phase coupling is present in healthy voice in larger extend as opposed to deformed (pathological) speech. This fact is observed because the pathological voice is characterized by vocal tract which is more markedly linear than in healthy speech [7]. The observation of non-linear phenomenon in speech signal is possible by bicoherence function which is defined [3]:…”

Section: Higher-order Statistical Methodsmentioning

confidence: 99%

Analysis of Polish Pathological Speech by Higher Order Spectrum

Wszołek

Kłaczyński

2010

Acta Phys. Pol. A

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Section: Higher-order Statistical Methodsmentioning

confidence: 99%

Analysis of Polish Pathological Speech by Higher Order Spectrum

Wszołek

Kłaczyński

2010

Acta Phys. Pol. A

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“…Outras técnicas são aplicadas no sentido de evitar esse problema, tais como laringoscopia, glotografia, estroboscopia, electromiografia e videoquimografia (Kukharchik et al, 2007).É possível diagnosticar com precisão as mais diversas patologias da laringe a partir dessas técnicas. O problemaé que elas são invasivas, causando muito desconforto ao paciente, o que gera resistência por parte deste no momento em que se efetua o exame, podendo causar distorções nos dados obtidos e, assim, produzir falsos diagnósticos (Adnene and Lamia, 2003;Alonso et al, 2001). …”

Section: Introductionunclassified

Detecção Automática De Patologias Da Laringe Usando Codificação Por Predição Linear E Redes Neurais MLP

Marinus¹,

Gomes²,

Macedo³

2016

Anais Do 9. Congresso Brasileiro De Redes Neurais

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Abstract-The laryngeal diseases affect many professionals who use their voices as the main working tool, such as teachers, for example. Advanced diagnosis techniques of these diseases are typically invasive, causing much discomfort to the patient. In recent years techniques of Digital Speech Processing has been investigated to obtain non-invasive systems to aid the diagnosis by a specialist. The work presented proposes a method of analysis that uses coefficients obtained from Linear Prediction Coding to represent the voice signals and Multilayer Perceptron Neural Networks for classification between normal voice and pathological voice. An experimental evaluation of the method has demonstrated that this is a promising approach for discriminating between pathological and normal voices, reaching a correct classification rate above 98%.Keywords-Laryngeal Pathologies, Linear Prediction Coding, Multilayer Perceptron Neural Networks.Resumo-As patologias da laringe afetam muitos profissionais que têm a voz como principal instrumento de trabalho, tais como professores, por exemplo. Técnicas avançadas de diagnóstico dessas patologias são tipicamente invasivas, causando muito desconforto ao paciente. Nosúltimos anos, têm sido pesquisadas técnicas de Processamento Digital de Voz para obtenção de sistemas não invasivos para o auxílio ao diagnóstico por um especialista. O trabalho ora apresentado propõe um método de análise que utiliza coeficientes obtidos a partir da Codificação por Predição Linear para representação dos sinais de voz e Redes Neurais Multilayer Perceptron para classificação entre voz normal e voz patológica. Uma avaliação experimental demonstrou que se trata de um método promissor na discriminação entre voz normal e voz patológica, atingindo uma taxa de acerto superior a 98%.

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“…Human vocal tract is hardly accessible for visual examination during phonation process and that makes it more problematic to identify pathology. Moreover, these diagnostic means may cause the patients feel much discomfort and distort the actual signal so that it may be lead to incorrect diagnosis as well [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid of Genetic Algorithm and Support Vector Machine for Feature Reduction and Detection of Vocal Fold Pathology

Majidnezhad¹,

Kheidorov²

2013

IJIGSP

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Abstract-Acoustic analysis is a proper method in vocal fold pathology diagnosis so that it can complement and in some cases replace the other invasive, based on direct vocal fold observation, methods. There are d ifferent approaches and algorith ms for vocal fold pathology diagnosis. These algorithms usually have three stages which are Feature Extraction, Feature Reduction and Classification. While the third stage imp lies a choice of a variety of machine learning methods (Support Vector Machines, Artificial Neural Networks, etc), the first and second stages play a critical ro le in performance and accuracy of the classification system. In this paper we present initial study of feature ext raction and feature reduction in the task of vocal fold pathology diagnosis. A new type of feature vector, based on wavelet packet decomposition and Mel-Frequency-Cepstral-Coefficients (MFCCs), is proposed. Also a new GA -based method for feature reduction stage is proposed and compared with conventional methods such as Principal Co mponent Analysis (PCA). Support vector machine is used as a classifier for evaluating the performance of the proposed method. The results show the priority of the proposed method in co mparison with the current methods.

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Automatic Detection of Pathologies in The Voice by HOS Based Parameters

Cited by 91 publications

References 25 publications

Analysis of Polish Pathological Speech by Higher Order Spectrum

Analysis of Polish Pathological Speech by Higher Order Spectrum

Detecção Automática De Patologias Da Laringe Usando Codificação Por Predição Linear E Redes Neurais MLP

A Hybrid of Genetic Algorithm and Support Vector Machine for Feature Reduction and Detection of Vocal Fold Pathology

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