publications | Jorge A. Gómez-García

Thesis

2018

upm

Contributions to the Design of Automatic Voice Quality Analysis Systems using Speech Technologies

Jorge Andrés Gómez-García

Jan 2018

@thesis{gomez2018contributions,
  author = {Gómez-García, Jorge Andrés},
  doi = {10.20868/UPM.thesis.49565},
  institution = {Universidad Politécnica de Madrid},
  month = jan,
  title = {Contributions to the Design of Automatic Voice Quality Analysis Systems using Speech Technologies},
  year = {2018},
  url = {http://oa.upm.es/49565/},
}

2008

unal

Metodología para la caracterización de bioseales empleando análisis de complejidad sobre espacio de embebimiento no uniforme

Jorge Andrés Gómez-García

Jan 2008

Bib HTML

@thesis{gomezmetodologia,
  author = {Gómez-García, Jorge Andrés},
  institution = {Universidad Nacional de Colombia},
  title = {Metodología para la caracterización de bioseales empleando análisis de complejidad sobre espacio de embebimiento no uniforme},
  year = {2008},
  url = {https://repositorio.unal.edu.co/handle/unal/70145},
}

Papers

2021

BSPC
On the design of automatic voice condition analysis systems. Part III: review of acoustic modelling strategies

Jorge Andrés Gómez-García , Laureano Moro-Velázquez , Julián David Arias-Londoño , and 1 more author

Biomedical Signal Processing and Control, Apr 2021

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This is the third of a three-part series devoted to review the current state of the art of automatic voice condition analysis systems. A direct continuation to “On the design of automatic voice condition analysis systems. Part I: review of concepts and an insight to the state of the art” and to “On the design of automatic voice condition analysis systems. Part II: review of speaker recognition techniques and study on the effects of different variability factors” already published in this journal. The goal of this paper is to compile the most significant parameterisation approaches used in the literature for automatic voice condition analysis systems, along with a critical discussion about their usefulness, providing the user with a comprehensive review of the most important techniques used for acoustic modelling in the field. The paper presents the mathematical formulation and physical interpretation of a series of perturbation and fluctuation parameters, noise features, complexity based parameters, modulation spectra, morphological parameters, and spectral-cepstral coefficients; and is complemented with a library written in MATLAB®, which has been made available to the readers in an online software repository.
@article{gomez2021design, author = {Gómez-García, Jorge Andrés and Moro-Velázquez, Laureano and Arias-Londoño, Julián David and Godino-Llorente, Juan Ignacio}, title = {On the design of automatic voice condition analysis systems. Part III: review of acoustic modelling strategies}, journal = {Biomedical Signal Processing and Control}, publisher = {Elsevier}, volume = {66}, pages = {102049}, month = apr, year = {2021}, doi = {10.1016/j.bspc.2020.102049}, issn = {17468094}, }

2020

IEEE-ACM
Analysis of the Effects of Supraglottal Tract Surgical Procedures in Automatic Speaker Recognition Performance

Laureano Moro-Velázquez , Estefania Hernandez-Garcia , Jorge Andrés Gómez-García , and 2 more authors

IEEE/ACM Transactions on Audio, Speech, and Language Processing, Apr 2020

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This article evaluates the impact in the performance of state-of-The-Art automatic speaker recognition schemes of three surgical procedures modifying the supraglottal tract structures of speakers. To do so, a new corpus (Cuco) was recorded, containing the speech of 107 speakers before and after surgery. Speakers were divided into four groups depending on the type of surgery: Tonsillectomy, functional endoscopy sinus surgery (FESS), septoplasty, and controls. The analyzed speaker recognition schemes were i-vectors, i-vectors with supervised Universal Background Model, i-vectors employing Time-delay Deep Neural Networks and x-vectors. In all cases, probabilistic linear discriminant analysis was employed in the back-end. Results show changes in the speech of patients who underwent tonsillectomy or FESS after surgery in contrast to controls or patients who had a septoplasty, where not significant variations are observed. These changes increase the Equal Error Rate (EER) of the analyzed speaker recognition schemes for the septoplasty and FESS groups when employing enrollment data recorded before the surgery. Moreover, surgery has a similar influence in the speech of female and male speakers with respect to the analyzed schemes. In consequence, results suggest that it is advisable to update the speaker’s enrollment speech after three months following supraglottal tract surgery to ensure that the effects of the operation and post-operative recovery period do not influence the performance of the automatic speaker recognition systems.
@article{moro2020analysis, author = {Moro-Velázquez, Laureano and Hernandez-Garcia, Estefania and Gómez-García, Jorge Andrés and Godino-Llorente, Juan Ignacio and Dehak, Najim}, title = {Analysis of the Effects of Supraglottal Tract Surgical Procedures in Automatic Speaker Recognition Performance}, journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing}, publisher = {IEEE}, volume = {28}, pages = {798-812}, year = {2020}, doi = {10.1109/TASLP.2020.2967567}, issn = {2329-9290}, }
IEEE-Access
Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach

Julián David Arias-Londoño , Jorge Andrés Gómez-García , Laureano Moro-Velázquez , and 1 more author

IEEE Access, Apr 2020

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Current standard protocols used in the clinic for diagnosing COVID-19 include molecular or antigen tests, generally complemented by a plain chest X-Ray. The combined analysis aims to reduce the significant number of false negatives of these tests and provide complementary evidence about the presence and severity of the disease. However, the procedure is not free of errors, and the interpretation of the chest X-Ray is only restricted to radiologists due to its complexity. With the long term goal to provide new evidence for the diagnosis, this paper presents an evaluation of different methods based on a deep neural network. These are the first steps to develop an automatic COVID-19 diagnosis tool using chest X-Ray images to differentiate between controls, pneumonia, or COVID-19 groups. The paper describes the process followed to train a Convolutional Neural Network with a dataset of more than 79, 500 X-Ray images compiled from different sources, including more than 8, 500 COVID-19 examples. Three different experiments following three preprocessing schemes are carried out to evaluate and compare the developed models. The aim is to evaluate how preprocessing the data affects the results and improves its explainability. Likewise, a critical analysis of different variability issues that might compromise the system and its effects is performed. With the employed methodology, a 91.5% classification accuracy is obtained, with an 87.4% average recall for the worst but most explainable experiment, which requires a previous automatic segmentation of the lung region.
@article{arias2020artificial, author = {Arias-Londoño, Julián David and Gómez-García, Jorge Andrés and Moro-Velázquez, Laureano and Godino-Llorente, Juan Ignacio}, title = {Artificial Intelligence Applied to Chest X-Ray Images for the Automatic Detection of COVID-19. A Thoughtful Evaluation Approach}, journal = {IEEE Access}, publisher = {IEEE}, volume = {8}, pages = {226811-226827}, year = {2020}, doi = {10.1109/ACCESS.2020.3044858}, issn = {2169-3536}, }
IEEE-JSTSP
Multimodal and Multi-Output Deep Learning Architectures for the Automatic Assessment of Voice Quality Using the GRB Scale

Julián David Arias-Londoño , Jorge Andrés Gómez-García , and Juan Ignacio Godino-Llorente

IEEE Journal of Selected Topics in Signal Processing, Feb 2020

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This article addresses the automatic assessment of voice quality according to the GRB scale, based on the use of a variety of deep learning architectures for prediction purposes. The proposed architectures are multimodal, because they employ multiples sources of information; and also multi-output, because they simultaneously predict all the traits of the GRB scale. A feature engineering approach is followed, based on the use of deep neural networks and a set of well-established features such as MFCC, perturbation and complexity characteristics. Likewise, a representation learning is considered, using convolutional neural networks feed on modulation spectra extracted from voices. Finally, diverse loss functions are also investigated, including two surrogate ordinal classification, a conventional weighed categorical cross-entropy, and a mean square error function. Experiments are carried out in a dataset containing registers of the sustained phonation of three vowels. The best deep learning architecture provides a relative performance improvement of 6.25% for G, 14.1% for R and 18.1% for B, in comparison with recently published results using the same dataset.
@article{arias2020multimodal, author = {Arias-Londoño, Julián David and Gómez-García, Jorge Andrés and Godino-Llorente, Juan Ignacio}, title = {Multimodal and Multi-Output Deep Learning Architectures for the Automatic Assessment of Voice Quality Using the GRB Scale}, journal = {IEEE Journal of Selected Topics in Signal Processing}, publisher = {IEEE}, issue = {2}, volume = {14}, pages = {413-422}, month = feb, year = {2020}, doi = {10.1109/JSTSP.2019.2956410}, issn = {1932-4553}, }

2019

BSPC
On the design of automatic voice condition analysis systems. Part I: Review of concepts and an insight to the state of the art

Jorge Andrés Gómez-García , Laureano Moro-Velázquez , and Juan Ignacio Godino-Llorente

Biomedical Signal Processing and Control, May 2019

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This is the first of a two-part series devoted to review the current state of the art of automatic voice condition analysis systems. The goal of this paper is to provide to the scientific community and to newly comers to the field of automatic voice condition analysis a resource that presents introductory concepts, a categorisation of different aspects of voice pathology and a systematic literature review that describes the methodologies and methods that are mostly used in these systems. To this end, pathological voice is firstly described in terms of perceptual characteristics and its relationship with physiological phenomena. Then, a prototypical automatic voice condition analysis system is described, discussing each one of its constituting parts and presenting an in-depth literature review about the methodologies that are typically employed. Finally, a discussion about some variability factors that affect the performance of these systems is presented.
@article{gomez2019design2, author = {Gómez-García, Jorge Andrés and Moro-Velázquez, Laureano and Godino-Llorente, Juan Ignacio}, title = {On the design of automatic voice condition analysis systems. Part I: Review of concepts and an insight to the state of the art}, journal = {Biomedical Signal Processing and Control}, publisher = {Elsevier}, volume = {51}, pages = {181-199}, month = may, year = {2019}, doi = {10.1016/j.bspc.2018.12.024}, issn = {17468094}, }
EAAI
Emulating the perceptual capabilities of a human evaluator to map the GRB scale for the assessment of voice disorders

Jorge Andrés Gómez-García , Laureano Moro-Velázquez , Janaina Mendes-Laureano , and 2 more authors

Engineering Applications of Artificial Intelligence, Jun 2019

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This paper presents the design of an automatic voice quality analysis system for the assessment of voice pathologies, which emulates the perceptual capabilities of a human evaluator according the GRB scale. For this purpose, a novel methodology based on multiple sets of characteristics, ordinal classification and Gaussian regression is proposed. In particular, a reduced subset of characteristics is identified, and the regressor is used to convert the discrete perceptual scale to a continuum, more in agreement to the nature of the problem under study. The robustness of the system is evaluated in several cross-dataset experiments. Similarly, a clinical evaluation of the predictions provided by the system is carried out. Results indicate that the proposed methodology is proficient in modelling the perceptual capabilities of the human evaluator. They also show that it is possible to extend the GRB scale to a continuum through regression techniques while maintaining the consistency of the results. On average, the deviation between the labels assessed by the expert and the ones provided by the system is of about 0.5 units (in a scale from 0 to 3) for G and B, and of 0.7 units for R. Similarly, the deviation of the labels predicted by the system in the clinical assessment trials is about 0.3 units for G, 0.4 units for B, and 0.5 units for R.
@article{gomez2019emulating, author = {Gómez-García, Jorge Andrés and Moro-Velázquez, Laureano and Mendes-Laureano, Janaina and Castellanos-Domínguez, Germán and Godino-Llorente, Juan Ignacio}, title = {Emulating the perceptual capabilities of a human evaluator to map the GRB scale for the assessment of voice disorders}, journal = {Engineering Applications of Artificial Intelligence}, publisher = {Pergamon}, volume = {82}, pages = {236-251}, month = jun, year = {2019}, issn = {09521976}, doi = {10.1016/j.engappai.2019.03.027}, }

2018

ASC
Analysis of speaker recognition methodologies and the influence of kinetic changes to automatically detect Parkinson’s Disease

Applied Soft Computing, Jan 2018

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The diagnosis of Parkinson’s Disease is a challenging task which might be supported by new tools to objectively evaluate the presence of deviations in patient’s motor capabilities. To this respect, the dysarthric nature of patient’s speech has been exploited in several works to detect the presence of this disease, but none of them has deeply studied the use of state-of-the-art speaker recognition techniques for this task. In this paper, two classification schemes (GMM-UBM and i-Vectors-GPLDA) are employed separately with several parameterization techniques, namely PLP, MFCC and LPC. Additionally, the influence of the kinetic changes, described by their derivatives, is analysed. With the proposed methodology, an accuracy of 87% with an AUC of 0.93 is obtained in the optimal configuration. These results are comparable to those obtained in other works employing speech for Parkinson’s Disease detection and confirm that the selected speaker recognition techniques are a solid baseline to compare with future works. Results suggest that Rasta-PLP is the most reliable parameterization for the proposed task among all the tested features while the two employed classification schemes perform similarly. Additionally, results confirm that kinetic changes provide a substantial performance improvement in Parkinson’s Disease automatic detection systems and should be considered in the future.
@article{Moro2018, title = {Analysis of speaker recognition methodologies and the influence of kinetic changes to automatically detect Parkinson's Disease}, journal = {Applied Soft Computing}, pages = {649-666}, volume = {62}, month = jan, year = {2018}, issn = {15684946}, doi = {10.1016/j.asoc.2017.11.001}, }

2017

PlosOne
Towards the identification of Idiopathic Parkinson’s Disease from the speech. New articulatory kinetic biomarkers

Juan Ignacio Godino-Llorente , Stephanie Shattuck-Hufnagel , Jeung-Yoon Choi , and 2 more authors

PLOS ONE, Dec 2017

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Although a large amount of acoustic indicators have already been proposed in the literature to evaluate the hypokinetic dysarthria of people with Parkinson’s Disease, the goal of this work is to identify and interpret new reliable and complementary articulatory biomarkers that could be applied to predict/evaluate Parkinson’s Disease from a diadochokinetic test, contributing to the possibility of a further multidimensional analysis of the speech of parkinsonian patients. The new biomarkers proposed are based on the kinetic behaviour of the envelope trace, which is directly linked with the articulatory dysfunctions introduced by the disease since the early stages. The interest of these new articulatory indicators stands on their easiness of identification and interpretation, and their potential to be translated into computer based automatic methods to screen the disease from the speech. Throughout this paper, the accuracy provided by these acoustic kinetic biomarkers is compared with the one obtained with a baseline system based on speaker identification techniques. Results show accuracies around 85% that are in line with those obtained with the complex state of the art speaker recognition techniques, but with an easier physical interpretation, which open the possibility to be transferred to a clinical setting.
@article{Godino-Llorente2017, author = {Godino-Llorente, Juan Ignacio and Shattuck-Hufnagel, Stephanie and Choi, Jeung-Yoon and Moro-Velázquez, Laureano and Gómez-García, Jorge Andrés}, title = {Towards the identification of Idiopathic Parkinson’s Disease from the speech. New articulatory kinetic biomarkers}, journal = {PLOS ONE}, editor = {Jäncke, Lutz}, issue = {12}, volume = {12}, pages = {e0189583}, month = dec, year = {2017}, issn = {1932-6203}, doi = {10.1371/journal.pone.0189583}, pmid = {29240814}, }
UdeM
Análisis de la influencia de las técnicas de compresión de voz en la detección de anomalías vocales

Lina María Sepúlveda Cano , Jhon Jair Quiza Montealegre , and Jorge Andrés Gómez-García

Revista Ingenierías Universidad de Medellín, Dec 2017

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This paper compares the results of using compressed voice signals versus uncompressed speech signals to automatically detect voice abnormalities. Coding techniques and voice compression used in this study are the same as those used by default in the fixed, mobile and ip telephony systems, and techniques of characterization and classification used are also among the most used for detecting automatic speech abnormalities. The results obtained indicate that it is possible to use compressed voice signals for automatic detection of vocal pathologies without compromising the success rate in the diagnosis, which would make the implementation of automatic remote diagnosis of vocal pathologies possible.
@article{sepulveda2017analisis, author = {Cano, Lina María Sepúlveda and Montealegre, Jhon Jair Quiza and Gómez-García, Jorge Andrés}, title = {Análisis de la influencia de las técnicas de compresión de voz en la detección de anomalías vocales}, journal = {Revista Ingenierías Universidad de Medellín}, publisher = {Universidad de Medell\{\'\i\}n}, issue = {30}, volume = {16}, pages = {49-66}, year = {2017}, issn = {16923324}, doi = {10.22395/rium.v16n30a3}, }

2016

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

Jorge Andrés Gómez-García , Laureano Moro-Velázquez , Juan Ignacio Godino-Llorente , and 1 more author

Revista Facultad de Ingeniería Universidad de Antioquia, Jun 2016

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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.
@article{gomez2016UAntioquia, author = {Gómez-García, Jorge Andrés and Moro-Velázquez, Laureano and Godino-Llorente, Juan Ignacio and Castellanos-Domínguez, Germán}, title = {An insight to the automatic categorization of speakers according to sex and its application to the detection of voice pathologies: A comparative study}, journal = {Revista Facultad de Ingeniería Universidad de Antioquia}, issue = {79}, pages = {50-62}, month = jun, year = {2016}, issn = {01206230}, doi = {10.17533/udea.redin.n79a06}, }
UdeM
Spectrum Sensing Framework based on Blind Source Separation for Cognitive Radio Environments

Lina María Sepúlveda Cano , Jhon Jair Quiza Montealegre , Camilo Gil Taborda , and 1 more author

Revista Ingenierías Universidad de Medellín, Jun 2016

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Cognitive radio (CR) is proposed as a key solution to improve spectral efficiency and overcome the spectrum scarcity. Spectrum sensing is an important task in each CR system with the aim of identifying the spectrum holes and using them for secondary user’s (SU) communications. Several conventional methods for spectrum sensing have been proposed such as energy detection, matched filter detection, etc. However, the main limitation of these classical methods is that the CR network is not able to communicate with its own base station during the spectrum sensing period and thus a fraction of the available primary frame cannot be exploited for data transmission. The other limitation in conventional methods is that the SU data frames should be synchronized with the primary network data frames. To overcome the above limitations, here, we propose a spectrum sensing technique based on blind source separation (BSS) that does not need time synchronization between the primary network and the CR. Moreover, by using the proposed technique, the SU can maintain its transmission with the base station even during spectrum sensing and thus higher rates are achieved by the CR network. Simulation results indicate that the proposed method outperforms the accuracy of conventional BSS-based spectrum sensing techniques. © 2013 KSII.
@article{sepulveda2016spectrum, author = {Cano, Lina María Sepúlveda and Montealegre, Jhon Jair Quiza and Taborda, Camilo Gil and Gómez-García, Jorge Andrés}, title = {Spectrum Sensing Framework based on Blind Source Separation for Cognitive Radio Environments}, journal = {Revista Ingenierías Universidad de Medellín}, issue = {29}, volume = {15}, pages = {129-140}, year = {2016}, issn = {16923324}, doi = {10.22395/rium.v15n29a8}, }
UdeA
An insight to the automatic categorization of speakers according to sex and its application to the detection of voice pathologies: A comparative study

Jorge Andrés Gómez-García , Laureano Moro-Velázquez , Juan Ignacio Godino-Llorente , and 1 more author

Revista Facultad de Ingeniería Universidad de Antioquia, Jun 2016

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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.
@article{gomez2016, author = {Gómez-García, Jorge Andrés and Moro-Velázquez, Laureano and Godino-Llorente, Juan Ignacio and Castellanos-Domínguez, Germán}, title = {An insight to the automatic categorization of speakers according to sex and its application to the detection of voice pathologies: A comparative study}, journal = {Revista Facultad de Ingeniería Universidad de Antioquia}, publisher = {Universidad de Antioquia, Facultad de Ingenieria}, issue = {79}, pages = {50-63}, month = jun, year = {2016}, issn = {01206230}, doi = {10.17533/udea.redin.n79a06}, }
Voice pathology detection using modulation spectrum optimized metrics

L. Moro-Velazquez , J.A. Gómez-Garcı́a , and J.I Godino-Llorente

Frontiers in Bioengineering and Biotechnology, Jun 2016

2015

BiomedResearch
Modulation Spectra Morphological Parameters: A New Method to Assess Voice Pathologies according to the GRBAS Scale

Laureano Moro-Velázquez , Jorge Andrés Gómez-García , Juan Ignacio Godino-Llorente , and 1 more author

BioMed Research International, Jun 2015

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Disordered voices are frequently assessed by speech pathologists using perceptual evaluations. This might lead to problems caused by the subjective nature of the process and due to the influence of external factors which compromise the quality of the assessment. In order to increase the reliability of the evaluations, the design of automatic evaluation systems is desirable. With that in mind, this paper presents an automatic system which assesses the Grade and Roughness level of the speech according to the GRBAS perceptual scale. Two parameterization methods are used: one based on the classic Mel-Frequency Cepstral Coefficients, which has already been used successfully in previous works, and other derived from modulation spectra. For the latter, a new group of parameters has been proposed, named Modulation Spectra Morphological Parameters : MSC, DRB, LMR, MSH, MSW, CIL, PALA, and RALA. In methodology, PCA and LDA are employed to reduce the dimensionality of feature space, and GMM classifiers to evaluate the ability of the proposed features on distinguishing the different levels. Efficiencies of 81.6% and 84.7% are obtained for Grade and Roughness, respectively, using modulation spectra parameters, while MFCCs performed 80.5% and 77.7%. The obtained results suggest the usefulness of the proposed Modulation Spectra Morphological Parameters for automatic evaluation of Grade and Roughness in the speech.
@article{Moro-Velazquez2015, author = {Moro-Velázquez, Laureano and Gómez-García, Jorge Andrés and Godino-Llorente, Juan Ignacio and Andrade-Miranda, Gustavo}, title = {Modulation Spectra Morphological Parameters: A New Method to Assess Voice Pathologies according to the GRBAS Scale}, journal = {BioMed Research International}, publisher = {Hindawi Publishing Corporation}, editor = {Klein, Adam}, volume = {2015}, pages = {259239}, year = {2015}, issn = {2314-6133}, pmid = {26557656}, doi = {10.1155/2015/259239}, }
An automatic method to detect and track the glottal gap from high speed videoendoscopic images

G. Andrade-Miranda , J.I. Godino-Llorente , L. Moro-Velázquez , and 1 more author

Biomedical engineering online, Jun 2015

2014

Non uniform Embedding based on Relevance Analysis with reduced computational complexity: Application to the detection of pathologies from biosignal recordings

J.A Gómez-Garcı́a , J. I. Godino-Llorente , and G. Castellanos-Domı́nguez

Neurocomputing, Jun 2014

2010

ITM
Análisis de la tolerancia al ruido de características basadas en dinámica no lineal sobre señales fonocardiográficas

Carolina Ospina-Aguirre , Jorge Andrés Gómez-García , Edilson Delgado-Trejos , and 1 more author

TecnoLógicas, Dec 2010

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Una propiedad muy deseable en sistemas de detección automática de patologías es la robustez ante el ruido, tal que su presencia no afecte notablemente la habilidad de detectar patologías. El presente trabajo estudia la capacidad discriminatoria que características de la dinámica no lineal, en particular, el Exponente de Hurst, el Máximo Exponente de Lyapunov y la Dimensión de Correlación, puedan proveer en la detección de soplos cardíacos usando señales fonocardiográficas contaminadas con diferentes niveles de ruido. A su vez se hacen comparaciones con características obtenidas a partir de representaciones tiempo frecuencia. Los resultados muestran la fortaleza de las características basadas en dinámica no lineal para tareas de clasificación de estados funcionales, aún sobre señales con altos niveles de ruido.
@article{ospina2010analisis, title = {Análisis de la tolerancia al ruido de características basadas en dinámica no lineal sobre señales fonocardiográficas}, author = {Ospina-Aguirre, Carolina and Gómez-García, Jorge Andrés and Delgado-Trejos, Edilson and Castellanos-Domínguez, Germán}, journal = {TecnoLógicas}, publisher = {Instituto Tecnológico Metropolitano (ITM)}, issue = {25}, pages = {189}, month = dec, year = {2010}, issn = {2256-5337}, doi = {10.22430/22565337.135}, }

2008

Conferences

2021

IberSPEECH
New tools for the differential evaluation of Parkinson’s disease using voice and speech processing

Laureano Moro-Velázquez , Jorge Andrés Gómez-García , Najim Dehak , and 1 more author

In , Mar 2021

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Parkinson’s Disease (PD) is a neurodegenerative condition that affects the motor capabilities of individuals. Early detection can potentially contribute to slow its progression in a near future. Therefore, new objective and reliable tools are needed to support its diagnosis. Literature suggests that the patients’ speech can provide relevant information about the presence of the disease. In this study, five sets of experiments were carried out, each containing new approaches to detect the presence of the disease in the speech of idiopathic PD patients and control speakers from three parkinsonian corpora, two of them in the Spanish language. Different speech frame selection techniques are proposed, such as phonemic and acoustic landmark distillation, providing certain specific speech segments of interest to this work’s purposes. Multiple cepstral and spectral features were employed, along with several classification techniques based on Gaussian models and speaker embeddings. The best accuracy results in detecting PD with the proposed method-ologies reached values ranging from 85% to 94% with Area Under the Curve between 0.91 and 0.99, depending on the corpus. Results suggest that PD affects the movements related to all of the studied articulatory segmental groups but has a more evident influence in the consonants with a greater narrowing of the vocal tract, mainly plosives and fricatives. The new proposed methodologies demonstrate their ability to support PD’s diagnosis during a patient’s clinical assessment and are a step forward in PD’s speech-based diagnosis systems.
@inproceedings{moro2021new, author = {Moro-Velázquez, Laureano and Gómez-García, Jorge Andrés and Dehak, Najim and Godino-Llorente, Juan Ignacio}, city = {Valladolid, Spain}, doi = {10.21437/IberSPEECH.2021-36}, journal = {IberSPEECH 2021}, month = mar, pages = {165-169}, publisher = {ISCA}, title = {New tools for the differential evaluation of Parkinson's disease using voice and speech processing}, year = {2021}, }

2020

Predicting UPDRS Scores in Parkinson’s Disease Using Voice Signals: A Deep Learning/Transfer-Learning-Based Approach

Julián David Arias-Londoño , and Jorge Andrés Gómez-García

In Automatic Assessment of Parkinsonian Speech , Mar 2020

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In the last years, literature exhibits successful results in the automatic detection of Parkinson’s disease using voice/speech, especially for patients in medium or late stages of the disorder. By contrast, the prediction of the UPDRS scores -used to assess the severity of the disorder or the efficacy of treatments- has been shown to perform mostly poor. These results could be explained by the need of more complex machine learning models compared to the detection case, and the lack of large databases for properly training artificial intelligence models. To analyse possible solutions to these problems, this work will explore the potentiality of Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models, along transfer learning approaches, for the automatic prediction of the UPDRS scores. Experiments are carried out using feature engineering and feature learning methodologies. In particular for feature engineering, a series of well-know features that are used to characterise vocal conditions are employed to train a DNN. Likewise, the feature learning approach is based on transformation of the input speech using Modulation spectra transformations to train a CNN, considering a transfer learning approach. For transfer learning, the networks will be trained using voice signals from patients of databases of organic and functional voice pathologies; following a network architecture that has been proven successful recently for voice quality assessment using the GRB scale. The approach includes the combination of feature learning and feature engineering approaches using a multimodal strategy. The fine-tuning procedure of the last layers in the second network will be carried out using two databases of PD patients. The results present insights about the potential of deep learning along with transfer learning strategies for the prediction of UPDRS score in parkinsonian speechs.
AAPS
Approaches to Evaluate Parkinsonian Speech Using Artificial Models

Juan Ignacio Godino-Llorente , Laureano Moro-Velázquez , Jorge Andrés Gómez-García , and 3 more authors

In Automatic Assessment of Parkinsonian Speech , Mar 2020

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Our preliminary data and experiments show the potentiality of the artificial intelligence techniques to identify Parkinson’s disease and to assess its extent using the information extracted from the speech. Our conclusions are based on several prospective studies with different corpora of parkinsonian and control subjects modelled following different approaches, mainly grounded on articulatory aspects of the speech and on the use of probabilistic generative models inspired in those used in the field of speaker recognition. Results obtained are consistent among the different approaches and corpora, providing detection accuracies over 85%. This chapter compiles and compares the approaches we followed and the results we obtained in the last few years, and discusses some key aspects for future research in the field. The goal is not only to demonstrate the potentiality of the artificial intelligence techniques used, but also to extract from them significant knowledge that might be of interest for our understanding of the effects of the disease on the speech. To this respect a phonemic analysis based on the automatic techniques developed is carried out, suggesting important cues about the acoustic units that are more affected by the disease.
@inproceedings{godino2019approaches, author = {Godino-Llorente, Juan Ignacio and Moro-Velázquez, Laureano and Gómez-García, Jorge Andrés and Choi, Jeung-Yoon and Dehak, Najim and Shattuck-Hufnagel, Stephanie}, journal = {Communications in Computer and Information Science}, keywords = {Automatic detection,Hypokinetic dysarthria,Kinematic analysis,Parkinson’s Disease,Phonemic analysis,Speech}, title = {Approaches to Evaluate Parkinsonian Speech Using Artificial Models}, booktitle = {Automatic Assessment of Parkinsonian Speech}, publisher = {Springer International Publishing}, institution = {Springer, Cham}, volume = {1295}, pages = {77-99}, year = {2020}, doi = {10.1007/978-3-030-65654-6_5}, isbn = {9783030656539}, issn = {18650937}, }

2019

MAVEBA
Analysis of phonatory features for the automatic detection of Parkinson’s disease in two different corpora

Laureano Moro-Velázquez , Jorge Andrés Gómez-García , Najim Dehak , and 1 more author

In , Mar 2019

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In this study, several automatic detectors of Parkinson’s Disease (PD) based on phonatory aspects were analyzed employing two different corpora containing speech from speakers with PD. The features employed to characterize phonation were jitter, shimmer, noise measurements, complexity, modulation spectrum features and perceptual linear predictive coefficients. To differentiate between speakers with and without PD a gaussian mixture model classification scheme was used. Then, the approach providing the best results was combined with a scheme using articulatory information of the speech in order to assess the complementarity between phonatory and articulatory aspects in the automatic detection of PD. Cross-validation trials (k-folds) employing exclusively phonatory information provided accuracies between 64% and 71%, with AUC between 0.68 and 0.80 depending on the corpus. Results suggest that a combination of all the analyzed features with a PCA dimensionality reduction produce the best accuracy, AUC and sensibility. Also, results indicate that phonatory approaches tend to be less accurate in PD detection than other articulatory approaches proposed in previous studies. Finally, results suggest the complementarity between the studied articulatory and phonatory approaches is low.
@inproceedings{moro2019analysis, author = {Moro-Velázquez, Laureano and Gómez-García, Jorge Andrés and Dehak, Najim and Godino-Llorente, Juan Ignacio}, title = {Analysis of phonatory features for the automatic detection of Parkinson’s disease in two different corpora}, journal = {Models and Analysis of Vocal Emissions for Biomedical Applications - 11th International Workshop, MAVEBA 2019}, publisher = {Firenze University Press}, pages = {33-36}, year = {2019}, city = {Florence}, isbn = {9788864539614}, doi = {10.36253/978-88-6453-961-4}, }

2018

IEEE-BigData
ByoVoz Automatic Voice Condition Analysis System for the 2018 FEMH Challenge

Julián David Arias-Londoño , Jorge Andrés Gómez-García , Laureano Moro-Velázquez , and 1 more author

In , Dec 2018

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This paper presents the methods and results used by the ByoVoz team for the design of an automatic voice condition analysis system, which was submitted to the 2018 Far East Memorial Hospital voice data challenge. The proposed methodology is based on a cascading scheme that firstly discriminates between pathological and normophonic voices, and then identifies the type of disorder. By using diverse feature selection techniques, a subset of complexity, spectral/cepstral and perturbation characteristics were identified for the proposed tasks. Then, several generative classification methodologies based on Gaussian Mixture Models and Gradient Boosting were employed to provide decisions about the input voices in the binary classification, and using one-vs-one classification systems based on Random Forests for the categorization according to the type of disorder. By using a 4-folds cross-validation approach on the training partition a sensitivity=0.93 and specificity=0.74 was obtained. Similarly, an unweighted average recall of 0.63 and an accuracy of 66% was obtained for the identification task. Using the scoring metric proposed in the challenge the final resulting score is of 0.77. When the testing partition was considered, a sensitivity of 0.92, specificity of 0.54, unweighted average recall of 0.61 and a score of 0.72 were obtained. This was the fifth best result in the competition.
@inproceedings{arias2018byovoz, author = {Arias-Londoño, Julián David and Gómez-García, Jorge Andrés and Moro-Velázquez, Laureano and Godino-Llorente, Juan Ignacio}, title = {ByoVoz Automatic Voice Condition Analysis System for the 2018 FEMH Challenge}, institution = {IEEE}, journal = {2018 IEEE International Conference on Big Data (Big Data)}, publisher = {IEEE}, pages = {5228-5232}, month = dec, year = {2018}, isbn = {978-1-5386-5035-6}, doi = {10.1109/BigData.2018.8622498}, }
IEEE-EMBC
Study of the Automatic Detection of Parkison’s Disease Based on Speaker Recognition Technologies and Allophonic Distillation

Laureano Moro-Velázquez , Jorge Andrés Gómez-García , Juan Ignacio Godino-Llorente , and 7 more authors

In , Jul 2018

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The use of new tools to detect Parkinson’s Disease (PD) from speech articulatory movements can have a considerable impact in the diagnosis of patients. In this study, a novel approach involving speaker recognition techniques with allophonic distillation is proposed and tested separately in four parkinsonian speech databases (205 patients and 186 controls in total). This new scheme provides values between 72% and 94% of accuracy in the automatic detection of PD, depending on the database, and improvements up to 9% respect to baseline techniques. Results not only point towards the importance of the segmentation of the speech for the differentiation of parkinsonian and control speakers but confirm previous findings about the relevance of plosives and fricatives in the detection of parkinsonian dysarthria.
@inproceedings{moro2018study, author = {Moro-Velázquez, Laureano and Gómez-García, Jorge Andrés and Godino-Llorente, Juan Ignacio and Rusz, Jan and Skodda, Sabine and Grandas, Francisco and Velazquez, J. M. and Orozco-Arroyave, Juan Rafael and Noth, Elmer and Dehak, Najim}, title = {Study of the Automatic Detection of Parkison’s Disease Based on Speaker Recognition Technologies and Allophonic Distillation}, journal = {2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, publisher = {IEEE}, institution = {IEEE}, volume = {2018-July}, month = jul, pages = {1404-1407}, year = {2018}, isbn = {978-1-5386-3646-6}, issn = {1557170X}, doi = {10.1109/EMBC.2018.8512562}, pmid = {30440655}, }

2017

MAVEBA
On the design of a voice pathology assessment system based on the GRB scale.

Jorge Andrés Gómez-García , Laureano Moro-Velázquez , Janaina Mendes-Laureano , and 1 more author

In , Dec 2017

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The purpose of this paper is to present some preliminary results of an automatic system capable of modelling the perceptual abilities of a speech therapist that describes vocal quality in accordance to the GRB scale. The system is trained using three databases that have been evaluated by the same evaluator. 11 spectral, cepstral, modulation spectra and perturbation features are extracted from the input speech. Filter ranking algorithms are utilized to select the most consistent set of features among databases. Decision making is carried out using ordinal classification, to account for the ordering in the GRB scale, and Gaussian regression, to provide continuous decisions about voice quality. Results indicate that the proposed system is proficient when modelling, either by means of the Gaussian regressor and the ordinal classifier, the perceptual abilities of the evaluator. On average the deviations from the actual and predicted label are about half an unit.
@inproceedings{gomez2017_MAVEBA, author = {Gómez-García, Jorge Andrés and Moro-Velázquez, Laureano and Mendes-Laureano, Janaina and Godino-Llorente, Juan Ignacio}, title = {On the design of a voice pathology assessment system based on the GRB scale.}, journal = {Proceedings and Report - 10th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2017}, publisher = {Firenze University Press}, volume = {122}, pages = {3-6}, month = dec, year = {2017}, city = {Florence}, isbn = {9788864536064}, doi = {10.36253/978-88-6453-961-4}, editor = {Manfredi, Claudia}, }
MAVEBA
Use of acoustic landmarks and GMM-UBM blend in the automatic detection of Parkinson’s disease

Laureano Moro-Velázquez , Juan Ignacio Godino-Llorente , Jorge Andrés Gómez-García , and 3 more authors

In , Dec 2017

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New tools based on speech analysis can improve and accelerate diagnosis of Parkinson’s Disease. In this work, the use some specific segments of speech, around the so called Acoustic Landmarks, are used with different families of features such as acoustic cues or Rasta-PLP and GMM-UBM-Blend classification methods to detect Parkinson’s Disease. Results of 87% of accuracy are obtained. Burst segments provide the most relevant information when detecting Parkinson’s Disease while GMM-UBM-Blend is revealed as a promising technique when using small databases and segmented speech.
@inproceedings{moro2017use, author = {Moro-Velázquez, Laureano and Godino-Llorente, Juan Ignacio and Gómez-García, Jorge Andrés and Villalba, Jesús and Shattuck-Hufnagel, Stephanie and Dehak, Najim}, title = {Use of acoustic landmarks and GMM-UBM blend in the automatic detection of Parkinson’s disease}, journal = {Proceedings and Report - 10th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2017}, publisher = {Firenze University Press}, volume = {122}, pages = {73-76}, month = dec, year = {2017}, city = {Florence}, isbn = {9788864536064}, doi = {10.36253/978-88-6453-961-4}, editor = {Manfredi, Claudia}, }

2016

Detection of Parkinson’s disease by means of GMM-UBM and iVectors techniques

J-A. Gómez-Garcı́a , L. Moro-Velázquez , and J-I. Godino-Llorente

In XXXI Simposium Nacional de la Unión Científica Internacional de Radio, URSI 2016 , Dec 2016

2015

Tuning of modulation spectrum parameters for voice pathology detection

L. Moro-Velázquez , J.A Gómez-García , and J. I. Godino-Llorente

In 9th International Workshop on Models and Analysis of Vocal Emissions for Biomedial Applications , Dec 2015
Automatic Age Detection in Normal and Pathological Voice

J-A Gómez-Garcı́a , L Moro-Velázquez , J.I. Godino-Llorente , and 1 more author

In Sixteenth Annual Conference of the International Speech Communication Association , Dec 2015

2014

IberSPEECH

Analysis of complexity and modulation spectra parameterizations to characterize voice roughness

Laureano Moro-Velázquez , Jorge Andrés Gómez-García , and Juan Ignacio Godino-Llorente

In , Oct 2014

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@inproceedings{moro2014analysis,
  author = {Moro-Velázquez, Laureano and Gómez-García, Jorge Andrés and Godino-Llorente, Juan Ignacio},
  title = {Analysis of complexity and modulation spectra parameterizations to characterize voice roughness},
  journal = {IberSpeech 2014, VIII Jornadas en Tecnología del Habla and IV Iberian SLTech Workshop},
  pages = {1-10},
  month = oct,
  day = {19-21},
  year = {2014},
  isbn = {978-84-617-2862-6},
  url = {https://www.researchgate.net/profile/Juan-L-Navarro-Mesa/publication/321621643_Advances_in_Speech_and_Language_Technologies_for_Iberian_Languages_Second_International_Conference_IberSPEECH_2014_Las_Palmas_de_Gran_Canaria_Spain_November_19-21_2014_Proceedings/links/61dde7f6323a2268f9997f71/Advances-in-Speech-and-Language-Technologies-for-Iberian-Languages-Second-International-Conference-IberSPEECH-2014-Las-Palmas-de-Gran-Canaria-Spain-November-19-21-2014-Proceedings.pdf#page=73},
}

2010

Evaluation of feature extraction techniques on event-related potentials for detection of attention-deficit/hyperactivity disorder

P.A Castro-Cabrera , J.A. Gómez-García , F. Restrepo , and 2 more authors

In Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE , 31 2010-sept. 4 2010