Advances in a Multimodal Approach for Dysphagia Analysis Based on Automatic Voice Analysis

Ipiña, Karmele López de; Satue-Villar, Antonio; Faundez-Zanuy, Marcos; Arreola, Viridiana; Ortega, Omar; Clavé, Père; Sanz-Cartagena, M. Pilar; Mekyska, Jiří; Calvo, Pilar M.

doi:10.1007/978-3-319-33747-0_20

Cited by 11 publications

(14 citation statements)

References 19 publications

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“…Impaired speech production is frequently represented by dysphonia or dysphonic voice. Dysphonia is a perceptual quality of the voice that indicates that some negative changes have occurred in the phonation organs [1]. The relationship between voice pathology and acoustic voice features has been clinically established and confirmed both quantitatively and subjectively by speech experts [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Approach to Dysphonia Detection

et al. 2018

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This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech features and used these to train the classification model. As classifiers, three state-of-the-art methods were used: K-nearest neighbors, random forests, and support vector machine. We analyzed the performance of classifiers with and without gender taken into account. The experimental results showed that it is possible to recognize pathological speech with as high as a 91.3% classification accuracy.

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Section: Introductionmentioning

confidence: 99%

“…This is usually due to damage of the neurological system or caused directly by damage to some part of the vocal tract, such as the vocal cords [7]. Frequently, a speech disorder leads to a secondary symptom, and early detection may reveal many high-risk illnesses [1,8].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach to Dysphonia Detection

et al. 2018

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“…Furthermore, this proposal is based on the description of speech pathologies [5,12] with regard to articulation, phonation, quality of the speech, human perception, and the complex dynamics of the system. In this paper, some of the most used speech features (linear and nonlinear) will be taken into account for differentiation between pathological and healthy and speech [4,5,[12][13][14][15][16], and discrimination [11] through human perception. Most of them are well known in the field of pathological speech characterization, and therefore for each parameter, a reference is given where further information and a deeper description can be found.…”

Section: Methodsmentioning

confidence: 99%

“…N neurons, a convolution mask of cxc, and a pool mask of pxp. We have used the WEKA software suite [16] in order to perform the experimentation.…”

Section: Convolutional Neural Network (Cnn) With L Layers Ofmentioning

confidence: 99%

On the analysis of speech and disfluencies for automatic detection of Mild Cognitive Impairment

Ipiña

Martinez-de-Lizarduy

Calvo

et al. 2018

Neural Comput & Applic

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Alzheimer's disease is characterized by a progressive and irreversible cognitive deterioration. In a previous stage, the socalled Mild Cognitive Impairment or cognitive loss appears. Nevertheless, this previous stage does not seem sufficiently severe to interfere in independent abilities of daily life, so it is usually diagnosed inappropriately. Thus, its detection is a crucial challenge to be addressed by medical specialists. This paper presents a novel proposal for such early diagnosis based on automatic analysis of speech and disfluencies, and Deep Learning methodologies. The proposed tools could be useful for supporting Mild Cognitive Impairment diagnosis. The Deep Learning approach includes Convolutional Neural Networks and nonlinear multifeature modeling. Additionally, an automatic hybrid methodology is used in order to select the most relevant features by means of nonparametric Mann-Whitney U test and Support Vector Machine Attribute evaluation.

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“…Besides, this approach is based on the description of speech pathologies with regard to phonation, articulation, speech quality, human perception, and the complex dynamics of the system. In this work, we will use some of the most efficient speech features for differentiation between healthy and pathological speech in the state of the art (Gómez-Vilda et al, 2015;López-de-Ipiña et al, 2016;López-de-Ipiña et al, 2017). Most of them are well known in the field of speech signal processing, and thus for each parameter a reference is provided where a deeper description and further information can be found.…”

Section: Protocols and Algorithmsmentioning

confidence: 99%

<em>ALZUMERIC</em>: A decision support system for diagnosis and monitoring of cognitive impairment

Lizarduy

Salomón

Gómez-Vilda

et al. 2017

loquens

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Internet of things and smart cities are becoming a reality. Nowadays, more and more devices are interconnected and in order to deal with this new situation, data processing speeds are increasing to keep the pace. Smart devices like tablets and smartphones are accessible to a wide part of society in developed countries, and Internet connections for data exchange make it possible to handle large volumes of information in less time. This new reality has opened up the possibility of developing client-server architectures focused on clinical diagnosis in real time and at a very low cost. This paper illustrates the design and implementation of the ALZUMERIC system that is oriented to the diagnosis of Alzheimer’s disease (AD). It is a platform where the medical specialist can gather voice samples through non-invasive methods from patients with and without mild cognitive impairment (MCI), and the system automatically parameterizes the input signal to make a diagnose proposal. Although this type of impairment produces a cognitive loss, it is not severe enough to interfere with daily life. The present approach is based on the description of speech pathologies with regard to the following profiles: phonation, articulation, speech quality, analysis of the emotional response, language perception, and complex dynamics of the system. Privacy, confidentiality and information security have also been taken into consideration, as well as possible threats that the system could suffer, so this first prototype of services offered by ALZUMERIC has been targeted to a predetermined number of medical specialists.

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Advances in a Multimodal Approach for Dysphagia Analysis Based on Automatic Voice Analysis

Cited by 11 publications

References 19 publications

Machine Learning Approach to Dysphonia Detection

Machine Learning Approach to Dysphonia Detection

On the analysis of speech and disfluencies for automatic detection of Mild Cognitive Impairment

<em>ALZUMERIC</em>: A decision support system for diagnosis and monitoring of cognitive impairment

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