Voice Pathology Detection on the Saarbrücken Voice Database with Calibration and Fusion of Scores Using MultiFocal Toolkit

Martínez, David; Lleida, Eduardo; Giménez, Alfonso Ortega; Miguel, Antonio; Villalba, Jesús

doi:10.1007/978-3-642-35292-8_11

Cited by 62 publications

(50 citation statements)

References 20 publications

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“…In particular, the accuracy rates in [39] are about 70%, when employing MFCC coefficients, noise related features, GMM classifiers and the vowel /a/ at normal pitch. Fusing vowels at different conditions (normal, high, low and rising-falling pitch), accuracy raised to 72%.…”

Section: Sex-dependent Voice Pathology Detectormentioning

confidence: 99%

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An insight to the automatic categorization of speakers according to sex and its application to the detection of voice pathologies: A comparative study

García

Moro-Velázquez

Godino-Llorente

et al. 2016

Rev. Fac. Ing. Antioquia

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An automatic categorization of the speakers according to their sex improves the performance of an automatic detector of voice pathologies. This is grounded on findings demonstrating perceptual, acoustical and anatomical differences in males' and females' voices. In particular, this paper follows two objectives: 1) to design a system which automatically discriminates the sex of a speaker when using normophonic and pathological speech, 2) to study the influence that this sex detector has on the accuracy of a further voice pathology detector. The parameterization of the automatic sex detector relies on MFCC applied to speech; and MFCC applied to glottal waveforms plus parameters modeling the vocal tract. The glottal waveforms are extracted from speech via iterative lattice inverse filters. Regarding the pathology detector, a MFCC parameterization is applied to speech signals. Classification, in both sex and pathology detectors, is carried out using state of the art techniques based on universal background models. Experiments are performed in the Saarbrücken database, employing the sustained phonation of vowel /a/. Results indicate that the sex of the speaker may be discriminated automatically using normophonic and pathological speech, obtaining accuracy up to 95%. Moreover, including the a-priori information about the sex of the speaker produces an absolute performance improvement in EER of about 2% on pathology detection tasks. RESUMEN:Una categorización automática de los hablantes de acuerdo con su sexo mejora el rendimiento de un detector automático de patologías de voz. Esto se fundamenta en hallazgos que demuestran diferencias perceptuales, acústicas y anatómicas en voces masculinas y femeninas. En particular, este trabajo persigue dos objetivos: 1) diseñar un sistema que discrimine automáticamente el sexo de hablantes utilizando habla normofónica y patológica, 2) estudiar la influencia que este detector de sexo tiene sobre el acierto de un posterior detector de patologías de voz. La parametrización del detector automático de sexo se basa en MFCC aplicados sobre señales de voz; y MFCC aplicados a formas de onda glotal junto a parámetros que modelan el tracto vocal. Las formas de onda glotal se extraen de la voz a través de un filtrado inverso iterativo en celosía. En cuanto al detector de patologías, una parametrización MFCC se aplica a señales de voz. La clasificación, tanto en los detectores de sexo como de patología, se lleva a cabo con técnicas del estado del arte basadas en modelos de base universal. Experimentos son realizados sobre la base de datos Saarbrücken empleando la fonación sostenida de la vocal /a/. Los resultados indican que el sexo del hablante puede ser discriminado automáticamente utilizando habla normofónica y

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Section: Sex-dependent Voice Pathology Detectormentioning

confidence: 99%

“…Fusing vowels at different conditions (normal, high, low and rising-falling pitch), accuracy raised to 72%. In addition, the literature reports that fusing information from other vowels at different pitch conditions, and in over-optimistic scenarios, accuracies could reach 90% [39,40].…”

Section: Sex-dependent Voice Pathology Detectormentioning

confidence: 99%

An insight to the automatic categorization of speakers according to sex and its application to the detection of voice pathologies: A comparative study

García

Moro-Velázquez

Godino-Llorente

et al. 2016

Rev. Fac. Ing. Antioquia

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“…For example, Martínez et al in [7] use Gaussian mixture model (GMM) on Saarbruecken Voice Database, and achieved 67% classification accuracy with neutral sustained vowel /a/. However, with enhanced computational abilities of hardware and improvement of machine learning algorithms, deep neural network (DNN)-hidden Markov model (HMM) is gradually replacing the traditional GMM-HMM [8] to become the popular method for speech recognition.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for Pathological Voice Detection Using Convolutional Deep Belief Networks

Wu¹,

Soraghan²,

Lowit

et al. 2018

Interspeech 2018

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Automatically detecting pathological voice disorders such as vocal cord paralysis or Reinke's edema is a challenging and important medical classification problem. While deep learning techniques have achieved significant progress in the speech recognition field there has been less research work in the area of pathological voice disorders detection. A novel system for pathological voice detection using convolutional neural network (CNN) as the basic architecture is presented in this work. The novel system uses spectrograms of normal and pathological speech recordings as the input to the network. Initially Convolutional deep belief network (CDBN) are used to pre-train the weights of CNN system. This acts as a generative model to explore the structure of the input data using statistical methods. Then a CNN is trained using supervised back-propagation learning algorithm to fine tune the weights. It will be shown that a small amount of data can be used to achieve good results in classification with this deep learning approach. A performance analysis of the novel method is provided using real data from the Saarbrucken Voice database.

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“…In addition, sustained phonation is rather unaffected by aspects related to different languages. Research concerning suitability of different vowels [20,21] often concludes that vowel /a/ results in the lowest EER in laryngeal pathology detection.…”

Section: Introductionmentioning

confidence: 99%

A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data

et al. 2017

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Abstract:The aim of this study is a transparent tool for analysis of voice (sustained phonation /a/) and query data capable of providing support in screening for laryngeal disorders. In this work, screening is concerned with identification of potentially pathological cases by classifying subject's data into 'healthy' and 'pathological' classes as well as visual exploration of data and automatic decisions. A set of association rules and a decision tree, techniques lending themselves for exploration, were generated for pathology detection. Data pairwise similarities, estimated in a novel way, were mapped onto a 2D metric space for visual inspection and analysis. Accurate identification of pathological cases was observed on unseen subjects using the most discriminative query parameter and six audio parameters routinely used by otolaryngologists in a clinical practice: equal error rate (EER) of 11.1% was achieved using association rules and 10.2% using the decision tree. The EER was further reduced to 9.5% by combining results from these two classifiers. The developed solution can be a useful tool for Otolaryngology departments in diagnostics, education and exploratory tasks.

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Voice Pathology Detection on the Saarbrücken Voice Database with Calibration and Fusion of Scores Using MultiFocal Toolkit

Cited by 62 publications

References 20 publications

An insight to the automatic categorization of speakers according to sex and its application to the detection of voice pathologies: A comparative study

An insight to the automatic categorization of speakers according to sex and its application to the detection of voice pathologies: A comparative study

A Deep Learning Method for Pathological Voice Detection Using Convolutional Deep Belief Networks

A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data

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