The Application of a Genetic Algorithm in the Noninvasive Assessment of Vocal Nodules in Children

Szklanny, Krzysztof; Wrzeciono, Piotr

doi:10.1109/access.2019.2908313

Cited by 2 publications

(2 citation statements)

References 43 publications

(55 reference statements)

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“…In [263], glottal parameters computed by GIF were used to identify pathophysiological phonatory mechanisms for phonotraumatic and nonphonotraumatic vocal hyperfunction. In [264], detection of pathological voices caused by vocal nodules was investigated using several glottal parameters and a classifier based on a genetic algorithm. In [265], automatic detection of voice pathology was studied by using a random forest classifier and including several voice disorders, both functional and organic pathologies.…”

Section: Study Of Pathological Voicesmentioning

confidence: 99%

Extraction and Utilization of Excitation Information of Speech: A Review

2021

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| Speech production can be regarded as a process where a time-varying vocal tract system (filter) is excited by a time-varying excitation. In addition to its linguistic message, the speech signal also carries information about, for example, the gender and age of the speaker. Moreover, the speech signal includes acoustical cues about several speaker traits, such as the emotional state and the state of health of the speaker. In order to understand the production of these acoustical cues by the human speech production mechanism and utilize this information in speech technology, it is necessary to extract features describing both the excitation and the filter of the human speech production mechanism. While the methods to estimate and parameterize the vocal tract system are well established, the excitation appears less studied. This article provides a review of signal processing approaches used for the extraction of excitation information from speech. This article highlights the importance of excitation information in the analysis and classification of phonation type and vocal emotions, in the analysis of nonverbal laughter sounds, and in studying pathological voices. Furthermore, recent developments of deep learning techniques in the context of extraction and utilization of the excitation information are discussed.

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Section: Study Of Pathological Voicesmentioning

confidence: 99%

Extraction and Utilization of Excitation Information of Speech: A Review

2021

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“…Hence the study is in line with existing literature and can be used as a screening tool for thyroid diagnosis. 17 The existing diagnosis methodologies are based on Blood test reports, imaging studies. Prominent studies are not based on a non-invasive thyroid acquisition methodologies.…”

Section: Introductionmentioning

confidence: 99%

Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers

Sai

Rajalakshmi

Snekhalatha

2021

Proc Inst Mech Eng H

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Thyroid is a butterfly shaped gland located in the neck region. Hormones are secreted by the thyroid gland that is responsible for various functions that maintain metabolism of the body. The variance in secretion of the hormones causes disorders such as Hyperthyroidism or Hypothyroidism. Electroglottography signal is a bio signal which represents the impedance that exist between the glottis regions. The study aims at design and development of an hardware circuit for the acquisition of Electroglottogram signal from normal and thyroid subjects is proposed followed by feature extraction from the acquired bio signal is performed. Further, machine learning classifiers were used to classify the normal and thyroid individuals. This modality of acquisition is non-invasive. Performance evaluation is done by testing various classifiers to study the accuracy. The classifiers tested were Random Forest, Random Tree, Bayes Net, Multilayer Perceptron, Simple Logistic classifier, and One-R classifier. Classifiers such as Random Forest, Random Tree, and Multilayer Perceptron showed high accuracy. The accuracy estimated by these classifiers was tested and its ROC curves with AUC scores were derived. The highest accuracy was reported for Simple Logistic classifier which was about 95.1%. Random Forest and Random Tree reported 93.5% and 91.9% respectively. Similarly, Multilayer Perceptron and Bayes Net gave 93.5% and 91.9%. The One-R classifier algorithm reported the lowest accuracy of 90.3% among the studied classifier algorithms. The ROC-AUC score for the classifiers were also reported to be more than 0.9 which is considered more promising and supports the acquisition and processing methodology. Hence the proposed technique can be efficiently used to diagnose thyroid non-invasively.

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The Application of a Genetic Algorithm in the Noninvasive Assessment of Vocal Nodules in Children

Cited by 2 publications

References 43 publications

Extraction and Utilization of Excitation Information of Speech: A Review

Extraction and Utilization of Excitation Information of Speech: A Review

Non-invasive thyroid detection based on electroglottogram signal using machine learning classifiers

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