Classification and analysis of speech abnormalities

Nayak, Jagadish; Bhat, P. Subbanna; Acharya, U. Rajendra; Aithal, U. V.

doi:10.1016/j.rbmret.2005.05.002

Cited by 35 publications

(17 citation statements)

References 10 publications

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“…Acoustic analysis is a routine procedure in voice evaluation to characterize the vocal quality, assist in differential diagnosis, document and monitor the voice parameters, and assess the outcomes of the treatment provided to the patient with a voice disorder 8 . Acoustic analysis employs computerbased techniques to measure the properties of the acoustic signal-whether from a vowel or connected speech-providing more objective information regarding the voice signal compared to the auditoryperceptual analysis, as it produces quantitative data in the voice assessment process [9][10][11][12][13] .…”

Section: Análise Acústica De Vozes Infantis: Contribuições Do Diagrammentioning

confidence: 99%

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Análise acústica de vozes infantis: contribuições do diagrama de desvio fonatório

et al. 2015

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Resumo:OBJETIVO:analisar o poder discriminatório do diagrama de desvio fonatório na avaliação do tipo de voz predominante e da intensidade do desvio vocal em crianças.MÉTODOS:coletou-se a vogal /ε/ sustentada de 93 crianças. A intensidade do desvio e a qualidade vocal foram analisadas por meio da escala analógico-visual. Utilizou-se o diagrama para a análise acústica, com avaliação da distribuição dos sinais vocais de acordo com a área, quadrante, forma e densidade. Realizou-se o teste de igualdade de proporções e o teste Qui-quadrado(x2) para comparar as variáveis, e o teste de Correlação de Spearman para correlacionar as medidas acústicas e perceptivo-auditivas.RESULTADOS: houve correlação entre a classificação de quadrantes e a intensidade do desvio vocal para todos os parâmetros analisados. Houve diferença estatisticamente significante entre a proporção de crianças com rugosidade, soprosidade, tensão e instabilidade em relação à área, ao quadrante e à forma. Não houve diferença estatisticamente significante entre a proporção de crianças com e sem desvio da qualidade vocal em relação a todos os parâmetros analisados, ao considerar a distribuição das vozes nos quadrantes.CONCLUSÃO: o diagrama de desvio fonatório foi capaz de diferenciar a qualidade vocal predominante por meio da distribuição nos quadrantes, embora não tenha discriminado as vozes infantis saudáveis e alteradas.

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Section: Análise Acústica De Vozes Infantis: Contribuições Do Diagrammentioning

confidence: 99%

“…In view of this, some studies [8][9][10][11][12] have indicated that a voice assessment should use a combination of several acoustic measures of perturbation and noise, so that each individual utterance is quantified by a set of attributes.…”

Section: Introductionmentioning

confidence: 99%

Análise acústica de vozes infantis: contribuições do diagrama de desvio fonatório

et al. 2015

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“…Additionally, the SOM application allowed the researchers to omit the tedious parameterization phase, which is commonly used in the speech analysis procedure (Chen et al, 1996;Czyżewski et al, 2003;Howell et al, 1997bHowell et al, , 1997aNatke et al, 2006;Nayak et al, 2005;Wiśniewski et al, 2007). Although there were no human-made references authors assumed that parameterization was performed correctly due to the fact that winning neuron numbers in time represent the general structure of the analysed fragment -its syllabic structure, silence moments, etc.…”

Section: The Generalizing Sommentioning

confidence: 99%

“…In addition, Kohonen's Self-organizing Maps are considered to be very useful as far as data compression and transformation are concerned (Kotti et al, 2008). Thanks to their features -input data generalization, distinguishing between very similar signals, etc., ANNs are widely used in the area of non-fluent speech recognition, especially in classification (Czyżewski et al, 2003;Howell and Sackin, 1995;Howell et al, 1997bHowell et al, , 1997aNayak et al, 2005), speech quality assessment (Geetha et al, 2000), quite often with the use of topological maps (Leinonen et al, 1993;Wszołek and Tadeusiewicz, 2000). The diversity of ANN applications at many stages of the speech recognition process indicates that artificial neural networks are effective in both signal recognition and classification.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical ANN system for stuttering identification

Wietlicka

Kuniszyk-JóKowiak

SmołKa

2013

Computer Speech & Language

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“…Voice disorders tend to disturb the sound signal in various ways, combining various types of perturbation and noise in vocal emissions [1-12]. Thus, studies that examine the use of combined acoustic measurements in voice signal assessment are clearly important.…”

Section: Introductionmentioning

confidence: 99%

Performance of Phonatory Deviation Diagrams in Synthesized Voice Analysis

Lopes

Silva

Evangelista

et al. 2017

Folia Phoniatr Logop

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Objective: To analyze the performance of a phonatory deviation diagram (PDD) in discriminating the presence and severity of voice deviation and the predominant voice quality of synthesized voices. Method: A speech-language pathologist performed the auditory-perceptual analysis of the synthesized voice (n = 871). The PDD distribution of voice signals was analyzed according to area, quadrant, shape, and density. Results: Differences in signal distribution regarding the PDD area and quadrant were detected when differentiating the signals with and without voice deviation and with different predominant voice quality. Differences in signal distribution were found in all PDD parameters as a function of the severity of voice disorder. Conclusion: The PDD area and quadrant can differentiate normal voices from deviant synthesized voices. There are differences in signal distribution in PDD area and quadrant as a function of the severity of voice disorder and the predominant voice quality. However, the PDD area and quadrant do not differentiate the signals as a function of severity of voice disorder and differentiated only the breathy and rough voices from the normal and strained voices. PDD density is able to differentiate only signals with moderate and severe deviation. PDD shape shows differences between signals with different severities of voice deviation.

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Classification and analysis of speech abnormalities

Cited by 35 publications

References 10 publications

Análise acústica de vozes infantis: contribuições do diagrama de desvio fonatório

Análise acústica de vozes infantis: contribuições do diagrama de desvio fonatório

Hierarchical ANN system for stuttering identification

Performance of Phonatory Deviation Diagrams in Synthesized Voice Analysis

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