Voice pathology distinction using autoassociative neural networks

Hemmerling, Daria

doi:10.23919/eusipco.2017.8081528

Cited by 9 publications

(8 citation statements)

References 24 publications

(31 reference statements)

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“…[36,17,26], etc. From the voice pathologies point of view, most researchers restricted the dataset to a limited set of pathologies [7,43,14,25,51,44,5,3,4,2].…”

Section: Introductionmentioning

confidence: 99%

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Towards robust voice pathology detection

Harár

Galáž

Alonso-Hernández

et al. 2018

Neural Comput & Applic

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Automatic objective non-invasive detection of pathological voice based on computerized analysis of acoustic signals can play an important role in early diagnosis, progression tracking and even effective treatment of pathological voices. In search towards such a robust voice pathology detection system we investigated 3 distinct classifiers within supervised learning and anomaly detection paradigms. We conducted a set of experiments using a variety of input data such as raw waveforms, spectrograms, mel-frequency cepstral coefficients (MFCC) and conventional acoustic (dysphonic) features (AF). In comparison with previously published works, this article is the first to utilize combination of 4 different databases comprising normophonic and pathological recordings of sustained phonation of the vowel /a/ unrestricted to a subset of vocal pathologies. Furthermore, to our best knowledge, this article is the first to explore gradient boosted trees and deep learning for this application. The following best classification performances measured by F1 score on dedicated test set were achieved: XGBoost (0.733) using AF and MFCC, DenseNet (0.621) using MFCC, and Isolation Forest (0.610) using AF. Even though these results are of exploratory character, conducted experiments do show promising potential of gradient boosting and deep learning methods to robustly detect voice pathologies.

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“…[36,17,26], etc. From the voice pathologies point of view, most researchers restricted the dataset to a limited set of pathologies [7,43,14,25,51,44,5,3,4,2].…”

Section: Introductionmentioning

confidence: 99%

“…After the feature extraction, multiple conventional classifiers have been used to detect the presence of voice pathology. Most authors relied on the following algorithms: Support Vector Machines (SVM), Gaussian Mixture Models (GMM), Random Forests (RF), and Artificial Neural Networks (ANN) [25,5,7,14], etc.…”

Section: Introductionmentioning

confidence: 99%

Towards robust voice pathology detection

Harár

Galáž

Alonso-Hernández

et al. 2018

Neural Comput & Applic

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“…Frequency-based features like MFCC, Cepstral-based features, HNR, and LP-based parameters were used to form a 14dimensional vector for each subgroup. Over 37 linear features were used to train neural network with 87.5% accuracy [6]. The authors believes that with more protuberant features describing dynamics of vocals cords along with better feature selection methods can enhance the accuracy of the LC detection system.…”

Section: Background Studymentioning

confidence: 99%

Voice-Disorder Identification of Laryngeal Cancer Patients

Gour¹,

Udayashankara²,

Badakh³

et al. 2020

IJACSA

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This Previous studies have shown that much of laryngeal cancer-based work was carried out with a minimal set of linear features. Much of the work was focused on the study of larynx preservation, quality of life around radiotherapy, or surgery. The voice disorder database was not solely limited to laryngeal cancer. In the context of this, the paper proposes a noninvasive voice disorder detection of laryngeal cancer patients. The sustained vowel /a/ was recorded with 55 laryngeal cases and 55 healthy cases. Owing to the non-linearity property of the vocal cords, seven non-linear parameters along with biologically inspired 39 Mel-Frequency Cepstral Coefficients (MFCC) are extracted. This forms a laryngeal dataset of size 110X46. The wrapper method is used for better feature selection and to enhance the discriminating ability of the present work. The classification is carried out using a tuned support vector machine (SVM) with grid search and random forest (RF). The present work has shown an improved accuracy of 76.56% with SVM and 80% in the case of random forest. The forward selection of features along with the involvement of non-linear features has played a significant role in the better performance of the present system.

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“…The work in [11] performed correlation analyses on the sub-band signal to detect pathological voices. In addition, deep learning and convolutional neural networks were investigated for pathological voice detection [12] – [18] . The recent works applied unsupervised domain adaptation to address the hardware variation [19] .…”

Section: Introductionmentioning

confidence: 99%

Continuous Speech for Improved Learning Pathological Voice Disorders

Wang

Lai

et al. 2022

IEEE Open J. Eng. Med. Biol.

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Numerous studies had successfully differentiated normal and abnormal voice samples. Nevertheless, further classification had rarely been attempted. This study proposes a novel approach, using continuous Mandarin speech instead of a single vowel, to classify four common voice disorders (i.e. functional dysphonia, neoplasm, phonotrauma, and vocal palsy). Methods: In the proposed framework, acoustic signals are transformed into mel-frequency cepstral coefficients, and a bi-directional long-short term memory network (BiLSTM) is adopted to model the sequential features. The experiments were conducted on a large-scale database, wherein 1,045 continuous speech were collected by the speech clinic of a hospital from 2012 to 2019. Results: Experimental results demonstrated that the proposed framework yields significant accuracy and unweighted average recall improvements of 78.12-89.27% and 50.92-80.68%, respectively, compared with systems that use a single vowel. Conclusions: The results are consistent with other machine learning algorithms, including gated recurrent units, random forest, deep neural networks, and LSTM. The sensitivities for each disorder were also analyzed, and the model capabilities were visualized via principal component analysis. An alternative experiment based on a balanced dataset again confirms the advantages of using continuous speech for learning voice disorders.

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Voice pathology distinction using autoassociative neural networks

Cited by 9 publications

References 24 publications

Towards robust voice pathology detection

Towards robust voice pathology detection

Voice-Disorder Identification of Laryngeal Cancer Patients

Continuous Speech for Improved Learning Pathological Voice Disorders

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