Automatic system to detect the type of voice pathology

Jothilakshmi, S.

doi:10.1016/j.asoc.2014.03.036

Cited by 34 publications

(18 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, it is stated that it is appropriate to take α around 0.95 [17]. It is also assumed in this study that the characteristics of the voice data change slowly in time.…”

Section: Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

A New Approach for Detection of Pathological Voice Disorders with Reduced Parameters

Ankishan¹

2018

IU-JEEE

View full text Add to dashboard Cite

“…In this study, it is stated that it is appropriate to take α around 0.95 [17]. It is also assumed in this study that the characteristics of the voice data change slowly in time.…”

Section: Preprocessingmentioning

confidence: 99%

“…For instance, [14] has helped to extract useful features. In their study, have shown that parameter reduction affects speech robustness in inner classificatios [17].…”

Section: Introduction Ankışhan H Voice Disorders Detectionmentioning

confidence: 99%

A New Approach for Detection of Pathological Voice Disorders with Reduced Parameters

Ankishan¹

2018

IU-JEEE

View full text Add to dashboard Cite

“…Closer attention can be given to measuring instabilities in the voice signal or the noise content and general articulatory problems. The most commonly used measures are fundamental frequency , jitter (frequency perturbation), shimmer (amplitude perturbation) (Farrus et al, 2007;Lieberman, 1963;Horii, 1980;Steinecke 632 D. Panek et al and Herzel, 1995), harmonic-to-noise ratio (Yumoto et al, 1982) and mel-frequency coefficients (Rabiner and Juang, 1993;Godino-Llorente et al, 2006a;Godino-Llorente and Gomez-Vilda, 2004;Steinecke and Herzel, 1995;Jothilakshmi, 2014;Saldanha et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Acoustic analysis assessment in speech pathology detection

Panek

Skalski

Gajda

et al. 2015

International Journal of Applied Mathematics and Computer Science

View full text Add to dashboard Cite

Automatic detection of voice pathologies enables non-invasive, low cost and objective assessments of the presence of disorders, as well as accelerating and improving the process of diagnosis and clinical treatment given to patients. In this work, a vector made up of 28 acoustic parameters is evaluated using principal component analysis (PCA), kernel principal component analysis (kPCA) and an auto-associative neural network (NLPCA) in four kinds of pathology detection (hyperfunctional dysphonia, functional dysphonia, laryngitis, vocal cord paralysis) using the a, i and u vowels, spoken at a high, low and normal pitch. The results indicate that the kPCA and NLPCA methods can be considered a step towards pathology detection of the vocal folds. The results show that such an approach provides acceptable results for this purpose, with the best efficiency levels of around 100%. The study brings the most commonly used approaches to speech signal processing together and leads to a comparison of the machine learning methods determining the health status of the patient.

show abstract

“…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%

“…Due to the modern requirements imposed on the clinics, a predetermined huge number of patients and the short time in which a doctor should make an examination, initiates the formation of new devices, algorithms to accelerate the diagnosis. The main method used by the medical community to evaluate a speech production system and diagnose pathologies is direct ones, which requires direct inspection of vocal folds and cause discomfort to the patient [1].…”

Section: Introductionmentioning

confidence: 99%

Voice pathology distinction using autoassociative neural networks

Hemmerling

2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

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.

show abstract

Automatic system to detect the type of voice pathology

Cited by 34 publications

References 10 publications

A New Approach for Detection of Pathological Voice Disorders with Reduced Parameters

A New Approach for Detection of Pathological Voice Disorders with Reduced Parameters

Acoustic analysis assessment in speech pathology detection

Voice pathology distinction using autoassociative neural networks

Contact Info

Product

Resources

About