Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas
Este trabajo busca determinar la arquitectura de red neuronal que pueda estimar las asimetrías de fase y amplitud en una voz NPVH, utilizando un modelo sintético para simular el movimiento del tracto vocal. El resultado será una red neuronal entrenada para predecir estas asimetrías, facilitando la d...
- Autores:
-
Rodriguez Rodriguez, Oscar Duván
Téllez Rincón, Santiago
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad Militar Nueva Granada
- Repositorio:
- Repositorio UMNG
- Idioma:
- spa
- OAI Identifier:
- oai:repository.unimilitar.edu.co:10654/45757
- Acceso en línea:
- http://hdl.handle.net/10654/45757
- Palabra clave:
- REDES NEURALES (COMPUTADORES)
INTELIGENCIA ARTIFICIAL
NPVH
neural network
Redes neuronales
Voz hiperfuncional no fonotraumática
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv |
Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas |
dc.title.translated.spa.fl_str_mv |
Implementation of an artificial neural network for the prediction of phase and amplitude asymmetries in the case of non-phonotraumatic hyperfunctional voices |
title |
Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas |
spellingShingle |
Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas REDES NEURALES (COMPUTADORES) INTELIGENCIA ARTIFICIAL NPVH neural network Redes neuronales Voz hiperfuncional no fonotraumática |
title_short |
Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas |
title_full |
Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas |
title_fullStr |
Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas |
title_full_unstemmed |
Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas |
title_sort |
Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticas |
dc.creator.fl_str_mv |
Rodriguez Rodriguez, Oscar Duván Téllez Rincón, Santiago |
dc.contributor.advisor.none.fl_str_mv |
Peñuela Calderon, Lina Maria Solaque Guzmán, Leonardo Enrique |
dc.contributor.author.none.fl_str_mv |
Rodriguez Rodriguez, Oscar Duván Téllez Rincón, Santiago |
dc.subject.lemb.spa.fl_str_mv |
REDES NEURALES (COMPUTADORES) INTELIGENCIA ARTIFICIAL |
topic |
REDES NEURALES (COMPUTADORES) INTELIGENCIA ARTIFICIAL NPVH neural network Redes neuronales Voz hiperfuncional no fonotraumática |
dc.subject.keywords.spa.fl_str_mv |
NPVH neural network |
dc.subject.proposal.spa.fl_str_mv |
Redes neuronales Voz hiperfuncional no fonotraumática |
description |
Este trabajo busca determinar la arquitectura de red neuronal que pueda estimar las asimetrías de fase y amplitud en una voz NPVH, utilizando un modelo sintético para simular el movimiento del tracto vocal. El resultado será una red neuronal entrenada para predecir estas asimetrías, facilitando la detección de patologías de voz de manera no invasiva. |
publishDate |
2023 |
dc.date.issued.none.fl_str_mv |
2023-08-02 |
dc.date.accessioned.none.fl_str_mv |
2024-02-14T15:05:06Z |
dc.date.available.none.fl_str_mv |
2024-02-14T15:05:06Z |
dc.type.local.spa.fl_str_mv |
Tesis/Trabajo de grado - Monografía - Pregrado |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
dc.type.coar.*.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
format |
http://purl.org/coar/resource_type/c_7a1f |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10654/45757 |
dc.identifier.instname.spa.fl_str_mv |
instname:Universidad Militar Nueva Granada |
dc.identifier.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional Universidad Militar Nueva Granada |
dc.identifier.repourl.spa.fl_str_mv |
repourl:https://repository.unimilitar.edu.co |
url |
http://hdl.handle.net/10654/45757 |
identifier_str_mv |
instname:Universidad Militar Nueva Granada reponame:Repositorio Institucional Universidad Militar Nueva Granada repourl:https://repository.unimilitar.edu.co |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.relation.references.spa.fl_str_mv |
Jackson-Menaldi, MC. La voz normal. Buenos Aires: Editorial Médica Panamericana; 1992 Petty TL, Enright PL. Simple office spirometry for primary care practitioners National Lung Health Education Program Web site. 2005. (Consultado el 28/02/2006.) Disponible en: http://www.nlhep.org/resources-medical.html\#review ¿Cómo se diagnostica una disfonía? (s/f). Seorl.net. Recuperado el 19 de septiembre de 2022, de https://seorl.net/como-se-diagnostica-una-disfonia Alzamendi, G. A., Peterson, S. D., Erath, B. D., Hillman, R. E., & Zañartu, M. (2021). Triangular body-cover model of the vocal folds with coordinated activation of five intrinsic laryngeal muscles with applications to vocal hyperfunction. arXiv preprint arXiv:2108.01115 Galindo GE, Peterson SD, Erath BD, et al. Modeling the pathophysiology of phonotraumatic vocal hyperfunction with a triangular glottal model of the vocal folds. J Speech Lang Hearing Res. 2017;60:2452–2471. https://doi.org/10.1044/2017\_jslhr-s-16-0412 Z. -H. Ling et al., "Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends," in IEEE Signal Processing Magazine, vol. 32, no. 3, pp. 35-52, May 2015, doi: 10.1109/MSP.2014.2359987 H. Ze, A. Senior and M. Schuster, "Statistical parametric speech synthesis using deep neural networks," 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 7962-7966, doi: 10.1109/ICASSP.2013.6639215. A. Morales and J. I. Yuz, "Reduced order modeling for glottal airflow estimation using a Kalman smoother," 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA), 2021, pp. 1-6, doi: 10.1109/ICAACCA51523.2021.9465282 Michael J. Bianco, Peter Gerstoft, James Traer, Emma Ozanich, Marie A. Roch, Sharon Gannot, and Charles-Alban Deledalle , "Machine learning in acoustics: Theory and applications", The Journal of the Acoustical Society of America 146, 3590-3628 (2019) https://doi.org/10.1121/1.5133944 L. A. F. Mendoza, E. Cataldo, M. Vellasco, M. A. Silva, A. D. O. Cañon and J. M. de Seixas, "Classification of voice aging using ANN and glottal signal parameters," 2010 IEEE ANDESCON, 2010, pp. 1-5, doi: 10.1109/ANDESCON.2010.5633362 Ibarra EJ, Parra JA, Alzamendi GA, Cortés JP, Espinoza VM, Mehta DD, Hillman RE, Zañartu M. Estimation of Subglottal Pressure, Vocal Fold Collision Pressure, and Intrinsic Laryngeal Muscle Activation From Neck-Surface Vibration Using a Neural Network Framework and a Voice Production Model. Front Physiol. 2021 Sep 1;12:732244. doi: 10.3389/fphys.2021.732244. PMID: 34539451; PMCID: PMC8440844. Y. Wang et al., “Silent Speech Decoding Using Spectrogram Features Based on Neuromuscular Activities,” Brain Sciences, vol. 10, no. 7, p. 442, Jul. 2020, doi: 10.3390/brainsci10070442. N. Y. -H. Wang et al., "Improving the Intelligibility of Speech for Simulated Electric and Acoustic Stimulation Using Fully Convolutional Neural Networks," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 184-195, 2021, doi: 10.1109/TNSRE.2020.3042655 Souza CL, Carvalho FM, Araujo TM, Reis EJ, Lima VM, Porto LA. Factors associated with vocal fold pathologies in teachers. Rev Saude Publica, 2011 Oct; 45 (5): 914-21 Jiménez Fandiño LH, Wuesthoff C, García-Reyes JC. Estado de los profesionales de la voz en Colombia. Acta otorrinolaringol cir cabeza cuello [Internet]. 31 de agosto de 2018 [citado 14 de marzo de 2022];40(2):120-7. Disponible en: https://www.revista.acorl.org.co/index.php/acorl/article/view/224 Asofono.co. Recuperado el 22 de agosto de 2022, de https://asofono.co/wp-content/uploads/2016/09/ICOV\_p58-63\_Reyes\_Prevalencia.pdf G. Gidaye, J. Nirmal, K. Ezzine and M. Frikha, "Effective Detection of Voice Dysfunction Using Glottic Flow Descriptors," 2019 Third International Conference on Inventive Systems and Control (ICISC), 2019, pp. 307-312, doi: 10.1109/ICISC44355.2019.9036362. Un dispositivo “wearable” para el seguimiento de pacientes con problemas en 22la voz. (2019, noviembre 18). Noticias de la Ciencia. https://noticiasdelaciencia.com/art/35318/un-dispositivo-wearable-para-el-seguimiento-de-pacientes-con-problemas-en-la-voz Verdolini, K., Rosen, C. A., & Branski, R. C. (Eds.). (2006). Classification manual for voice disorders-I. Erlbaum Hillman RE, Stepp CE, Van Stan JH, Zañartu M, Mehta DD. An Updated Theoretical Framework for Vocal Hyperfunction. Am J Speech Lang Pathol. 2020 Nov 12;29(4):2254-2260. doi: 10.1044/2020\_AJSLP-20-00104. Epub 2020 Oct 2. PMID: 33007164; PMCID: PMC8740570 Trastornos de la voz - Diagnóstico y tratamiento - Mayo Clinic. (2022). Retrieved 24 April2022, from https://www.mayoclinic.org/es-es/diseases-conditions/voice-disorders/diagnosis-treatment/drc-20353024 Carlos Calvache, Leonardo Solaque, Alexandra Velasco, Lina Peñuela, Biomechanical Models to Represent Vocal Physiology: A Systematic Review, Journal of Voice, 2021, , ISSN 0892-1997, https://doi.org/10.1016/j.jvoice.2021.02.014. (https://www.sciencedirect.com/science/article/pii/S0892199721000680 Mayo Foundation for Medical Education and Research (MFMER) (2022). 2022,from https://www.google.com/url?sa=t\&rct=j\&q=\&esrc=s\&source=web\&cd=\&cad=rja\&uact=8\&ved=2ahUKEwib2pvphsH2AhWlRDABHV7mCdYQFnoECBQQAw&url=https\%3A\%2F0\%2Fwww.mayoclinic.org\%2Fes-es\%2Fdiseases-conditions\%2Fvoice-disorders\%2Fsymptoms-causes\%2Fsyc-20353022&usg=AOvVaw01fA0iY0xWRNOdYQg16hF1 Nueva 2 Titze IR. Phonation threshold pressure: a missing link in glottal aerodynamics. J Acoust Soc Am. 1992; 91: 2926-35 J. Z. Lin, V. M. Espinoza, K. L. Marks, M. Zañartu and D. D. Mehta, "Improved Subglottal Pressure Estimation From Neck-Surface Vibration in Healthy Speakers Producing Non-Modal Phonation," in IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 2, pp. 449-460, Feb. 2020, doi: 10.1109/JSTSP.2019.2959267 Zañartu, M., Ho, J.C., Mehta, D.D., Hillman, R.E., & Wodicka, G.R. (2013). Subglottal Impedance-Based Inverse Filtering of Voiced Sounds Using Neck Surface Acceleration. IEEE Transactions on Audio, Speech, and Language Processing, 21, 1929-1939 Bugueño, M., & Humberto, A. R. (2021). ESTIMACIÓN DEL FLUJO DE AIRE GLOTAL MEDIANTE FILTRAJE Y SUAVIZAMIENTO DE KALMAN USANDO MODELOS DE BAJO ORDEN. https://repositorio.usm.cl/handle/11673/52705 J. H. Van Stan et al., "Differences in Daily Voice Use Measures Between Female Patients With Nonphonotraumatic Vocal Hyperfunction and Matched Controls", Journal of Speech, Language, and Hearing Research, vol. 64, n.º 5, pp. 1457–1470, mayo de 2021. Accedido el 8 de septiembre de 2022. [En línea]. Disponible: https://doi.org/10.1044/2021\_jslhr-20-00538 G. Fant, The Acoustic Theory of Speech Production (Mouton, The Hague,1960) M. Hirano,‘‘Morphological structure of the vocal cord as a vibrator and its variations,’’ Folia Phoniatr., 26,89–94 (1974). Brad H. Story, An overview of the physiology, physics and modeling of the sound source for vowels, Acoustical Science and Technology, 2002, Volume 23, Issue 4, Pages 195-206, Released on J-STAGE July 01, 2002, Online ISSN 1347-5177, Print ISSN 1346-3969, https://doi.org/10.1250/ast.23.195, https://www.jstage.jst.go.jp/article/ast/23/4/23\_4\_195/\_article/-char/en, Abstract: The vibration of the vocal folds produces the primary sound source for vowels. I. R. Titze, Principles of Voice Production (Prentice-Hall, Englewood Cliffs,NJ,1994). H. Hollien and G. P. Moore,‘‘Stroboscopic laminography of the larynx during phonation,’’ Acta Otolaryngol., 65,209–215 (1968) J. J. Jiang and I. R. Titze,‘‘A methodological study of hemilaryngeal phonation,’’ Laryngoscope, 103,872–882 (1993) B. Doval, C. Alessandro, and N. H. Bernardoni, \The spectrum of glottal ow models,"Acta Acustica united with Acustica, vol. 92, no. 6, pp. 1026-1046, 2006. G. Fant, Acoustic theory of speech production. Walter de Gruyter, 1970. A. E. Rosenberg, “Effect of glottal pulse shape on the quality of natural vowels," The Journal of the Acoustical Society of America, vol. 49, no. 1A, pp. 583-590, 1971 J. W. Van den Berg,‘‘Myoelastic-aerodynamic theory of voice production,’’ J. Speech Hear. Res., 1,227–244 (1958) J. L. Flanagan and L. Landgraf,‘‘Self-oscillating source for vocal tract synthesizers,’’ IEEE Trans. Audio Electroacoust., AU-16,57–64 (1968) B. H. Story and I. R. Titze,‘‘Voice simulation with a body-cover model of the vocal folds,’’ J. Acoust. Soc. Am., 97,1249–1260 (1995) M. Rothenberg, \A new inverse-filtering technique for deriving the glottal airflow waveform during voicing," The Journal of the Acoustical Society of America, vol. 53, no. 6, pp. 1632-1645, 1973. Lee, Wei-Meng. Python Machine Learning, John Wiley & Sons, Incorporated, 2019. ProQuest Ebook Central, https://ebookcentral-proquest-com.ezproxy.umng.edu.co/lib/umng-ebooks/detail.action?docID=5747364 K. P. Murphy, Machine learning: A probabilistic perspective. Cambridge, MA: MIT Press, 2012. J. A. Flores, Focus on artificial neural networks. Hauppauge, N.Y: Nova Science Publishers, 2011. Alpaydin, Ethem. Introduction to Machine Learning, MIT Press, 2014. ProQuest Ebook Central, https://ebookcentral-proquest-com.ezproxy.umng.edu.co/lib/umng-ebooks/detail.action?docID=3339851 "Vocología y Metacomunicación". VocologyCenter. https://vocologycenter.com (accedido el 26 de septiembre de 2022) V. Espinoza y M. Zañartu, \"Estudio Dinámico de Parámetros de Filtrado Inverso para el Seguimiento Ambulatorio de la Función Vocal", en IX CONGRESO IBEROAMERICANO DE ACÚSTICA, Valdivia, Chile, 1–3 de diciembre de 2014. Valdivia, 2014, p. 2-8. Field, A. (2017). Discovering Statistics Using IBM SPSS Statistics. SAGE Publications, Incorporated. Myers, J. L., & Well, A. (2007). Research Design and Statistical Analysis. Taylor & Francis Group. Zar, J. H. (2009). Biostatistical analysis (5a ed.). Pearson Education. Y. Bengio, A. Courville y I. Goodfellow, Deep Learning. MIT Press, 2016. Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980. Duchi, J., Hazan, E., & Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12(Jul), 2121-2159. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. (Capítulo 1.6 aborda funciones de pérdida y criterios de optimización) |
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Peñuela Calderon, Lina MariaSolaque Guzmán, Leonardo EnriqueRodriguez Rodriguez, Oscar DuvánTéllez Rincón, SantiagoIngeniero en Mecatrónica2024-02-14T15:05:06Z2024-02-14T15:05:06Z2023-08-02http://hdl.handle.net/10654/45757instname:Universidad Militar Nueva Granadareponame:Repositorio Institucional Universidad Militar Nueva Granadarepourl:https://repository.unimilitar.edu.coEste trabajo busca determinar la arquitectura de red neuronal que pueda estimar las asimetrías de fase y amplitud en una voz NPVH, utilizando un modelo sintético para simular el movimiento del tracto vocal. El resultado será una red neuronal entrenada para predecir estas asimetrías, facilitando la detección de patologías de voz de manera no invasiva.This work aims to determine the neural network architecture that can estimate phase and amplitude asymmetries in an NPVH voice, using a synthetic model to simulate the movement of the vocal tract. The result will be a neural network trained to predict these asymmetries, facilitating the detection of voice pathologies in a non-invasive manner.Pregradoapplicaction/pdfspahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 InternationalAcceso abiertohttp://purl.org/coar/access_right/c_abf2Implementación de una red neuronal artificial para la predicción de las asimetrías de fase y amplitud en el caso de voces hiperfuncionales no fonotraumáticasImplementation of an artificial neural network for the prediction of phase and amplitude asymmetries in the case of non-phonotraumatic hyperfunctional voicesREDES NEURALES (COMPUTADORES)INTELIGENCIA ARTIFICIALNPVHneural networkRedes neuronalesVoz hiperfuncional no fonotraumáticaTesis/Trabajo de grado - Monografía - Pregradoinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fIngeniería en MecatrónicaFacultad de IngenieríaUniversidad Militar Nueva GranadaJackson-Menaldi, MC. La voz normal. Buenos Aires: Editorial Médica Panamericana; 1992Petty TL, Enright PL. Simple office spirometry for primary care practitioners National Lung Health Education Program Web site. 2005. (Consultado el 28/02/2006.) Disponible en: http://www.nlhep.org/resources-medical.html\#review¿Cómo se diagnostica una disfonía? (s/f). Seorl.net. Recuperado el 19 de septiembre de 2022, de https://seorl.net/como-se-diagnostica-una-disfoniaAlzamendi, G. A., Peterson, S. D., Erath, B. D., Hillman, R. E., & Zañartu, M. (2021). Triangular body-cover model of the vocal folds with coordinated activation of five intrinsic laryngeal muscles with applications to vocal hyperfunction. arXiv preprint arXiv:2108.01115Galindo GE, Peterson SD, Erath BD, et al. Modeling the pathophysiology of phonotraumatic vocal hyperfunction with a triangular glottal model of the vocal folds. J Speech Lang Hearing Res. 2017;60:2452–2471. https://doi.org/10.1044/2017\_jslhr-s-16-0412Z. -H. Ling et al., "Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends," in IEEE Signal Processing Magazine, vol. 32, no. 3, pp. 35-52, May 2015, doi: 10.1109/MSP.2014.2359987H. Ze, A. Senior and M. Schuster, "Statistical parametric speech synthesis using deep neural networks," 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 7962-7966, doi: 10.1109/ICASSP.2013.6639215.A. Morales and J. I. Yuz, "Reduced order modeling for glottal airflow estimation using a Kalman smoother," 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA), 2021, pp. 1-6, doi: 10.1109/ICAACCA51523.2021.9465282Michael J. Bianco, Peter Gerstoft, James Traer, Emma Ozanich, Marie A. Roch, Sharon Gannot, and Charles-Alban Deledalle , "Machine learning in acoustics: Theory and applications", The Journal of the Acoustical Society of America 146, 3590-3628 (2019) https://doi.org/10.1121/1.5133944L. A. F. Mendoza, E. Cataldo, M. Vellasco, M. A. Silva, A. D. O. Cañon and J. M. de Seixas, "Classification of voice aging using ANN and glottal signal parameters," 2010 IEEE ANDESCON, 2010, pp. 1-5, doi: 10.1109/ANDESCON.2010.5633362Ibarra EJ, Parra JA, Alzamendi GA, Cortés JP, Espinoza VM, Mehta DD, Hillman RE, Zañartu M. Estimation of Subglottal Pressure, Vocal Fold Collision Pressure, and Intrinsic Laryngeal Muscle Activation From Neck-Surface Vibration Using a Neural Network Framework and a Voice Production Model. Front Physiol. 2021 Sep 1;12:732244. doi: 10.3389/fphys.2021.732244. PMID: 34539451; PMCID: PMC8440844.Y. Wang et al., “Silent Speech Decoding Using Spectrogram Features Based on Neuromuscular Activities,” Brain Sciences, vol. 10, no. 7, p. 442, Jul. 2020, doi: 10.3390/brainsci10070442.N. Y. -H. Wang et al., "Improving the Intelligibility of Speech for Simulated Electric and Acoustic Stimulation Using Fully Convolutional Neural Networks," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 184-195, 2021, doi: 10.1109/TNSRE.2020.3042655Souza CL, Carvalho FM, Araujo TM, Reis EJ, Lima VM, Porto LA. Factors associated with vocal fold pathologies in teachers. Rev Saude Publica, 2011 Oct; 45 (5): 914-21Jiménez Fandiño LH, Wuesthoff C, García-Reyes JC. Estado de los profesionales de la voz en Colombia. Acta otorrinolaringol cir cabeza cuello [Internet]. 31 de agosto de 2018 [citado 14 de marzo de 2022];40(2):120-7. Disponible en: https://www.revista.acorl.org.co/index.php/acorl/article/view/224Asofono.co. Recuperado el 22 de agosto de 2022, de https://asofono.co/wp-content/uploads/2016/09/ICOV\_p58-63\_Reyes\_Prevalencia.pdfG. Gidaye, J. Nirmal, K. Ezzine and M. Frikha, "Effective Detection of Voice Dysfunction Using Glottic Flow Descriptors," 2019 Third International Conference on Inventive Systems and Control (ICISC), 2019, pp. 307-312, doi: 10.1109/ICISC44355.2019.9036362.Un dispositivo “wearable” para el seguimiento de pacientes con problemas en 22la voz. (2019, noviembre 18). Noticias de la Ciencia. https://noticiasdelaciencia.com/art/35318/un-dispositivo-wearable-para-el-seguimiento-de-pacientes-con-problemas-en-la-vozVerdolini, K., Rosen, C. A., & Branski, R. C. (Eds.). (2006). Classification manual for voice disorders-I. ErlbaumHillman RE, Stepp CE, Van Stan JH, Zañartu M, Mehta DD. An Updated Theoretical Framework for Vocal Hyperfunction. Am J Speech Lang Pathol. 2020 Nov 12;29(4):2254-2260. doi: 10.1044/2020\_AJSLP-20-00104. Epub 2020 Oct 2. 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