Correlation Between Speech-Related Feature Spaces and Clinical Voice Disorders in Patients with Dysphagia

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dc.creatorFlórez-Gómez, Andrés Felipe
dc.creatorOrozco-Arroyave, Juan Rafael
dc.creatorRoldán-Vasco, Sebastián
dc.date2022-04-05
dc.date.accessioned2025-10-01T23:52:46Z
dc.descriptionDysphagia is defined as the difficulty to transport an alimentary bolus from the oral cavity to the stomach in a safe and effective way. Currently, dysphagia-related diagnosis methods are invasive and highly dependent on the examiner’s experience. Biosignal-based studies, such as those on voice and speech records, have been proposed to develop complementary diagnostic tools. Likewise, this study explores, in features extracted from voice and speech signals, the capacity to discriminate between healthy subjects and patients with swallowing disorders. For this purpose, the signals were recorded in a group of 30 healthy individuals and 45 dysphagic patients. The participants performed different voice tasks (sustained vowels) and speech tasks (text reading, monologue, and diadochokinetic exercises). The patient records were assigned labels of three clinical conditions: wet voice, dysphonic voice, and voice with undetermined alteration. Classical voice- and speech-related feature spaces were assessed using statistical tests, and it was found that features related to phonation, prosody, and diadochokinesia have potential as biomarkers for the discrimination of different alterations in patients with dysphagia. This is a preliminary study based on voice and speech signals for a non-invasive and objective diagnosis of dysphagia.en-US
dc.descriptionLa disfagia se define como la dificultad para transportar un bolo alimenticio de forma segura y efectiva desde la cavidad oral hasta el estómago. En la actualidad, los métodos para el diagnóstico de la disfagia son invasivos y altamente dependientes de la experiencia del personal asistencial cualificado. El estudio de las bioseñales, como lo son los registros de voz y habla, ha sido propuesto con el fin de desarrollar herramientas complementarias al diagnóstico. De esta manera, el presente trabajo tuvo como objetivo explorar, en características extraídas en señales de voz y habla, la capacidad de discriminación entre personas sanas y pacientes con trastornos deglutorios. Para ello se registraron señales en un grupo de 30 personas sanas y 45 pacientes diagnosticados con disfagia. Los participantes realizaron diferentes tareas de voz (vocales sostenidas) y de habla (texto leído, monólogo y ejercicios diadococinéticos). Los registros de los pacientes fueron etiquetados en tres condiciones clínicas: voz húmeda, voz disfónica y voz con alteración no determinada. Se evaluaron espacios de características clásicas asociadas al análisis de voz y habla a través de pruebas estadísticas hallándose que las características relacionadas a la fonación, prosodia y diadococinesia tienen potencial como biomarcadores para la discriminación de diferentes alteraciones en pacientes con disfagia. Este trabajo constituye una aproximación preliminar basada en el estudio de señales de voz y habla para un diagnóstico no invasivo y objetivo de la disfagia.es-ES
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dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2220
dc.identifier10.22430/22565337.2220
dc.identifier.urihttps://hdl.handle.net/20.500.12622/7814
dc.languagespa
dc.publisherInstituto Tecnológico Metropolitano (ITM)es-ES
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2220/2353
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2220/2354
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2220/2355
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/2220/2356
dc.relation/*ref*/L. Sura; A. Madhavan; G. Carnaby; M. Crary, “Dysphagia in the elderly: management and nutritional considerations”, Clin. Interv. Aging, vol. 2012, no. 7, pp. 287-298, Jul. 2012. https://doi.org/10.2147/CIA.S23404
dc.relation/*ref*/D. C. Wolf, “Dysphagia”, en Clinical Methods: The History, Physical, and Laboratory Examinations, 3a ed., Eds. Boston: Butterworths, 1990. https://www.ncbi.nlm.nih.gov/books/NBK408/
dc.relation/*ref*/A. Farri; A. Accornero; C. Burdese, “Social importance of dysphagia: its impact on diagnosis and therapy”, Acta Otorhinolaryngol Ital, vol. 27, no. 2, pp. 83–6, Abr. 2007. http://www.ncbi.nlm.nih.gov/pubmed/17608136
dc.relation/*ref*/O. Ortega; A. Martín; P. Clavé, “Diagnosis and Management of Oropharyngeal Dysphagia Among Older Persons, State of the Art”, J. Am. Med. Dir. Assoc., vol. 18, no. 7, pp. 576–582, Jul. 2017. https://doi.org/10.1016/j.jamda.2017.02.015
dc.relation/*ref*/Ministerio de Salud y Protección Social Oficina de Promoción Social, “Sala situacional de la Población Adulta Mayor”, Minist. Salud y Protección Soc., pp. 1-8, 2018. https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/DE/PS/sala-situacion-envejecimiento-2018.pdf
dc.relation/*ref*/S. E. Langmore, “Evaluation of oropharyngeal dysphagia: which diagnostic tool is superior?”, Curr. Opin. Otolaryngol. Head Neck Surg., vol. 11, no. 6, pp. 485–489, Dic. 2003. http://journals.lww.com/00020840-200312000-00014
dc.relation/*ref*/T. Warnecke et al., “The Safety of Fiberoptic Endoscopic Evaluation of Swallowing in Acute Stroke Patients”, Stroke, vol. 40, no. 2, pp. 482–486, Feb. 2009. https://doi.org/10.1161/STROKEAHA.108.520775
dc.relation/*ref*/S. Restrepo-Agudelo; S. Roldan-Vasco; L. Ramirez-Arbelaez; S. Cadavid-Arboleda; E. Perez-Giraldo; A. Orozco-Duque, “Improving surface EMG burst detection in infrahyoid muscles during swallowing using digital filters and discrete wavelet analysis”, J. Electromyogr. Kinesiol., vol. 35, pp. 1–8, Aug. 2017. https://doi.org/10.1016/j.jelekin.2017.05.001
dc.relation/*ref*/C. M. Steele et al., “Development of a Non-invasive Device for Swallow Screening in Patients at Risk of Oropharyngeal Dysphagia: Results from a Prospective Exploratory Study”, Dysphagia, vol. 34, no. 5, pp. 698–707, Oct. 2019. https://doi.org/10.1007/s00455-018-09974-5
dc.relation/*ref*/D. H. McFarland; P. Tremblay, “Clinical implications of cross-system interactions”, Semin. Speech Lang., vol. 27, no. 4, pp. 300–310, 2006. https://doi.org/10.1055/s-2006-955119
dc.relation/*ref*/D. Farneti, “Voice and Dysphagia”, en Dysphagia: Diagnosis and Treatment, O. Ekberg, Ed. Cham: Springer International Publishing, 2017, pp. 257–274. https://doi.org/10.1007/174_2017_110
dc.relation/*ref*/A. E. Aronson, Clinical voice disorders. Thieme Inc., 1990.
dc.relation/*ref*/T. Warms; J. Richards, “``Wet Voice’’ as a Predictor of Penetration and Aspiration in Oropharyngeal Dysphagia”, Dysphagia, vol. 15, no. 2, pp. 84–88, Mar. 2000. https://doi.org/10.1007/s004550010005
dc.relation/*ref*/S. Murugappan; S. Boyce; S. Khosla; L. Kelchner; E. Gutmark, “Acoustic characteristics of phonation in ‘wet voice’ conditions”, J. Acoust. Soc. Am., vol. 127, no. 4, pp. 2578–2589, Abr. 2010. https://doi.org/10.1121/1.3308478
dc.relation/*ref*/M. E. Dajer; P. R. Scalassara; J. L. Marrara; J. C. Pereira, “Voice analysis of patients with neurological disorders using acoustical and nonlinear tools”, IEEE Int. Work. Mach. Learn. Signal Process. MLSP, 2012. http://dx.doi.org/10.1109/mlsp.2012.6349803
dc.relation/*ref*/K. López-De-Ipiña et al., “Advances in a multimodal approach for dysphagia analysis based on automatic voice analysis”, en Smart Innovation, Systems and Technologies, 2016, vol. 54, pp. 201–211. https://doi.org/10.1007/978-3-319-33747-0_20
dc.relation/*ref*/J. S. Ryu; S. R. Park; K. H. Choi, “Prediction of laryngeal aspiration using voice analysis”, Am. J. Phys. Med. Rehabil., vol. 83, no. 10, pp. 753–757, Oct. 2004. http://dx.doi.org/10.1097/01.PHM.0000140798.97706.A5
dc.relation/*ref*/K. W. Dos Santos; B. Scheeren; A. C. Maciel; M. Cassol, “Vocal variability post swallowing in individuals with and without oropharyngeal dysphagia”, Int. Arch. Otorhinolaryngol., vol. 19, no. 1, pp. 61–66, 2015. https://doi.org/10.1055/s-0034-1394129
dc.relation/*ref*/J. R. Orozco-Arroyave et al., “NeuroSpeech: An open-source software for Parkinson’s speech analysis”, Digit. Signal Process. A Rev. J., vol. 77, pp. 207–221, Jun. 2018. https://doi.org/10.1016/j.dsp.2017.07.004
dc.relation/*ref*/J. R. Orozco-Arroyave; J. D. Arias-Londoño; J. F. Vargas-Bonilla; M. C. González-Rátiva; E. Nöth, “New Spanish speech corpus database for the analysis of people suffering from Parkinson’s disease”, Proc. 9th Int. Conf. Lang. Resour. Eval. Lr. 2014, pp. 342–347, 2014. http://www.lrec-conf.org/proceedings/lrec2014/pdf/7_Paper.pdf
dc.relation/*ref*/Y. Jadoul; B. Thompson; B. de Boer, “Introducing Parselmouth: A Python interface to Praat”, J. Phon., vol. 71, pp. 1–15, Nov. 2018. https://doi.org/10.1016/j.wocn.2018.07.001
dc.relation/*ref*/P. Boersma; D. Weenink, “Praat: doing phonetics by computer [Computer program]”. 2001, [En línea]. Disponible en: http://www.praat.org/
dc.relation/*ref*/J. C. Catford; J. H. Esling, “Phonetics, Articulatory”, en Encyclopedia of Language & Linguistics, Elsevier, 2006, pp. 425–442. https://doi.org/10.1016/B0-08-044854-2/00002-X
dc.relation/*ref*/F. R. Bach; M. I. Jordan, “Discriminative Training of Hidden Markov Models for Multiple Pitch Tracking [speech processing examples]”, en Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Philadelphia, 2005, vol. 5, pp. 489–492. http://doi.org/10.1109/ICASSP.2005.1416347
dc.relation/*ref*/P. Boersma, “Acurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound”, IFA Proc. 17, pp. 97–110, 1993. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.218.4956&rep=rep1&type=pdf
dc.relation/*ref*/S. Strömbergsson, “Today’s most frequently used F0 estimation methods, and their accuracy in estimating male and female pitch in clean speech”, Proc. Annu. Conf. Int. Speech Commun. Assoc. INTERSPEECH, pp. 525–529, Sep. 2016. http://dx.doi.org/10.21437/Interspeech.2016-240
dc.relation/*ref*/S. Basu; J. Chakraborty; M. Aftabuddin, “Emotion Recognition from Speech using Convolutional Neural Network with Recurrent Neural Network Architecture”, en 2nd International Conference on Communication and Electronics Systems (ICCES), Coimbatore 2017, pp. 333–336. https://doi.org/10.1109/CESYS.2017.8321292
dc.relation/*ref*/A. Shukla; R. Tiwari; R. Kala, “Speech Signal Analysis”, en Studies in Computational Intelligence, vol. 307, Springer, Berlin, Heidelberg, 2010, pp. 111–128. https://doi.org/10.1007/978-3-642-14344-1_5
dc.relation/*ref*/S. Skodda; W. Visser; U. Schlegel, “Vowel articulation in parkinson’s disease”, J. Voice, vol. 25, no. 4, pp. 467–472, Jul. 2011. https://doi.org/10.1016/j.jvoice.2010.01.009
dc.relation/*ref*/G. Fant, Acoustic theory of speech production. The Hague: Mouton, 1960.
dc.relation/*ref*/K. N. Stevens; A. S. House, “Development of a Quantitative Description of Vowel Articulation”, J. Acoust. Soc. Am., vol. 27, no. 3, pp. 484–493, May. 1955. https://doi.org/10.1121/1.1907943
dc.relation/*ref*/M. Blomgren; M. Robb; Y. Chen, “A note on vowel centralization in stuttering and nonstuttering individuals”, J. Speech, Lang. Hear. Res., vol. 41, no. 5, pp. 1042–1051, Oct. 1998. https://doi.org/10.1044/jslhr.4105.1042
dc.relation/*ref*/M. Guzmán, “Acústica Del Tracto Vocal”, 2010. https://www.logopediapsicologia.com/wp-content/uploads/acustica-del-tracto-vocal.pdf
dc.relation/*ref*/S. Davis; P. Mermelstein, “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences”, IEEE Trans. Acoust., vol. 28, no. 4, pp. 357–366, Ago. 1980. https://doi.org/10.1109/TASSP.1980.1163420
dc.relation/*ref*/L. Moro-Velázquez; J. A. Gómez-García; J. I. Godino-Llorente; J. Villalba; J. R. Orozco-Arroyave; N. Dehak, “Analysis of speaker recognition methodologies and the influence of kinetic changes to automatically detect Parkinson’s Disease”, Appl. Soft Comput., vol. 62, pp. 649–666, Jan. 2018. https://doi.org/10.1016/j.asoc.2017.11.001
dc.relation/*ref*/F. O. López-pabón; T. Arias-vergara; J. R. Orozco-Arroyave, “Cepstral Analysis and Hilbert- Huang Transform for Automatic Detection of Parkinson ’ s Disease”, TecnoLógicas, vol. 23, no. 47, pp. 93–108, Jan. 2020. https://doi.org/10.22430/22565337.1401
dc.relation/*ref*/B. B. Monson; E. J. Hunter; A. J. Lotto; B. H. Story, “The perceptual significance of high-frequency energy in the human voice”, Front. Psychol., vol. 5, no. 587, pp. 1–11, Jun. 2014. https://doi.org/10.3389/fpsyg.2014.00587
dc.relation/*ref*/E. Zwicker, “Subdivision of the Audible Frequency Range into Critical Bands (Frequenzgruppen)”, J. Acoust. Soc. Am., vol. 33, no. 2, pp. 248, feb. 1961. https://doi.org/10.1121/1.1908630
dc.relation/*ref*/E. Zwicker; E. Terhardt, “Analytical expressions for critical‐band rate and critical bandwidth as a function of frequency”, J. Acoust. Soc. Am., vol. 68, no. 5, pp. 1523–1525, Aug. 1998. https://doi.org/10.1121/1.385079
dc.relation/*ref*/J. R. Orozco-Arroyave et al., “Automatic detection of Parkinson’s disease in running speech spoken in three different languages”, J. Acoust. Soc. Am., vol. 139, no. 1, pp. 481-500, Jan. 2016. https://doi.org/10.1121/1.4939739
dc.relation/*ref*/P. Maragos; T. F. Quatieri; J. F. Kaiser, “Speech nonlinearities, modulations, and energy operators”, in [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, Toronto, 1991. https://doi.org/10.1109/ICASSP.1991.150366
dc.relation/*ref*/R. B. Randall; W. A. Smith, “Application of the Teager Kaiser Energy Operator to Machine Diagnostics”, en Conference: Tenth DST Group International Conference on Health and Usage Monitoring Systems (HUMS), Melbourne, 2017, pp. 26–28. https://www.researchgate.net/publication/316284738
dc.relation/*ref*/M. Tatham; K. Morton, “Speech Production: Prosody”, en Speech Production and Perception, London: Palgrave Macmillan UK, 2006, pp. 121–163. https://doi.org/10.1057/9780230513969_5
dc.relation/*ref*/S. Roldan-Vasco ; A. Orozco-Duque; J. C. Suarez-Escudero; J. R. Orozco-Arroyave , “Machine learning based analysis of speech dimensions in functional oropharyngeal dysphagia”, Comput. Methods Programs Biomed., vol. 208, p. 106248, Sep. 2021. https://doi.org/10.1016/j.cmpb.2021.106248
dc.relation/*ref*/K. López-de-Ipiña et al., “Automatic voice analysis for dysphagia detection”, Speech, Lang. Hear., vol. 21, no. 2, pp. 86–89, 2018. https://doi.org/10.1080/2050571X.2017.1369017
dc.relation/*ref*/J. R. Orozco-Arroyave; N. García; J. F. Vargas-Bonilla; E. Nöth, “Automatic Detection of Parkinson’s Disease from Compressed Speech Recordings”, en Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science, Springer, Cham, 2015, pp. 88–95. http://dx.doi.org/10.1007/978-3-319-24033-6_10
dc.rightsDerechos de autor 2022 TecnoLógicases-ES
dc.sourceTecnoLógicas; Vol. 25 No. 53 (2022); e2220en-US
dc.sourceTecnoLógicas; Vol. 25 Núm. 53 (2022); e2220es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectDysphagiaen-US
dc.subjectSpeech analysisen-US
dc.subjectVoice analysisen-US
dc.subjectBiosignal processingen-US
dc.subjectFeature extractionen-US
dc.subjectStatistical analysisen-US
dc.subjectDisfagiaes-ES
dc.subjectanálisis de vozes-ES
dc.subjectanálisis del hablaes-ES
dc.subjectprocesamiento de bioseñaleses-ES
dc.subjectextracción de característicases-ES
dc.subjectanálisis estadísticoes-ES
dc.titleCorrelation Between Speech-Related Feature Spaces and Clinical Voice Disorders in Patients with Dysphagiaen-US
dc.titleCorrelación entre espacios de características acústicas del habla y trastornos clínicos de la voz en pacientes con disfagiaes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES

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