Improved Algorithm for Pathological and Normal Voices Identification

There are a lot of papers on automatic classification between normal and pathological voices, but they have the lack in the degree of severity estimation of the identified voice disorders. Building a model of pathological and normal voices identification, that can also evaluate the degree of severity of the identified voice disorders among students. In the present work, we present an automatic classifier using acoustical measurements on registered sustained vowels /a/ and pattern recognition tools based on neural networks. The training set was done by classifying students’ recorded voices based on threshold from the literature. We retrieve the pitch, jitter, shimmer and harmonic-to-noise ratio values of the speech utterance /a/, which constitute the input vector of the neural network. The degree of severity is estimated to evaluate how the parameters are far from the standard values based on the percent of normal and pathological values. In this work, the base data used for testing the proposed algorithm of the neural network is formed by healthy and pathological voices from German database of voice disorders. The performance of the proposed algorithm is evaluated in a term of the accuracy (97.9%), sensitivity (1.6%), and specificity (95.1%). The classification rate is 90% for normal class and 95% for pathological class

Exploring differences between phonetic classes in Sleep Apnoea Syndrome Patients using automatic speech processing techniques

This work is part of an on-going collaborative project between the medical and signal processing communities to promote new research efforts on automatic OSA (Obstructive Apnea Syndrome) diagnosis. In this paper, we explore the differences noted in phonetic classes (interphoneme) across groups (control/apnoea) and analyze their utility for OSA detectio

Hondatze kognitibo arinaren detekzio goiztiarrerako hizketa ezagutza automatikoan oinarrituriko ekarpenak

302 p.Alzheimerdun gaixoengan, mintzamena ez ezik, erantzun emozionala ere kaltetu egiten da. Emozioak giza gogoaren arkitekturarekin zerikusia dituzten prozesu kognitiboak dira, eta erabakiak hartzearekin eta oroimenaren kudeaketa edota arretarekin zerikusia dute, eta aldi berean ere, horiek hertsiki lotuta dauden komunikazioarekin. Hortaz, erantzun eta kudeaketa emozionalak ere badira gaitzaren hasierako fase horietan nahasten diren beste komunikazio-elementu batzuk, eta disfluentzia bezala, emozio-erantzuna narriadura kognitiboa neurtzeko adierazlea izan daiteke.Hortaz, zenbait atazaren bidez sortutako ahots-laginen azterketak direla medio, disfluentzia eta emozio-erantzuna jaso daitezke. Hizkuntzarekiko independenteak diren parametroak bildu eta horien hizkeraren nahasmenduak ezaugarritu badaitezke, ekarpena lagungarria izan daiteke diagnostikoa egingo duten espezialistentzat.Lehengaiak ahots-laginak direnez, ingurune kliniko zein etxeko ingurunean egindako ataza desberdinen bidez grabazioak egin eta datu-baseak osatu dira, osasun-guneen irizpide etikoak kontuan hartuta eta. Datu-base horien ikerketaren bidez, galera kognitiboaren garapena neurtu, kuantifikatu, balioztatu eta sailkatu nahi da. Gaitzaren etapa desberdinak hautematen laguntzeko ekarpena egin nahi da, eta horretarako, hizkuntzarekiko independenteak diren parametroen azterketa automatikorako teknika eta metodologiak garatu dira. Mintzamen automatikoaren analisian oinarritutako multi-hurbilketa ez-lineala egin da, zeinak hizketa-analisian erabiltzen diren denborazko serieen konplexutasunaren neurtze kuantitatiboa eman diezaguke

Hondatze kognitibo arinaren detekzio goiztiarrerako hizketa ezagutza automatikoan oinarrituriko ekarpenak

302 p.Alzheimerdun gaixoengan, mintzamena ez ezik, erantzun emozionala ere kaltetu egiten da. Emozioak giza gogoaren arkitekturarekin zerikusia dituzten prozesu kognitiboak dira, eta erabakiak hartzearekin eta oroimenaren kudeaketa edota arretarekin zerikusia dute, eta aldi berean ere, horiek hertsiki lotuta dauden komunikazioarekin. Hortaz, erantzun eta kudeaketa emozionalak ere badira gaitzaren hasierako fase horietan nahasten diren beste komunikazio-elementu batzuk, eta disfluentzia bezala, emozio-erantzuna narriadura kognitiboa neurtzeko adierazlea izan daiteke.Hortaz, zenbait atazaren bidez sortutako ahots-laginen azterketak direla medio, disfluentzia eta emozio-erantzuna jaso daitezke. Hizkuntzarekiko independenteak diren parametroak bildu eta horien hizkeraren nahasmenduak ezaugarritu badaitezke, ekarpena lagungarria izan daiteke diagnostikoa egingo duten espezialistentzat.Lehengaiak ahots-laginak direnez, ingurune kliniko zein etxeko ingurunean egindako ataza desberdinen bidez grabazioak egin eta datu-baseak osatu dira, osasun-guneen irizpide etikoak kontuan hartuta eta. Datu-base horien ikerketaren bidez, galera kognitiboaren garapena neurtu, kuantifikatu, balioztatu eta sailkatu nahi da. Gaitzaren etapa desberdinak hautematen laguntzeko ekarpena egin nahi da, eta horretarako, hizkuntzarekiko independenteak diren parametroen azterketa automatikorako teknika eta metodologiak garatu dira. Mintzamen automatikoaren analisian oinarritutako multi-hurbilketa ez-lineala egin da, zeinak hizketa-analisian erabiltzen diren denborazko serieen konplexutasunaren neurtze kuantitatiboa eman diezaguke

Optimum Path Forest Classifier Applied To Laryngeal Pathology Detection

Optimum path forest-based classifiers are a novel approach for supervised pattern recognition. The OPF classifier differs from traditional approaches by not estimating probability density functions of the classes neither assuming samples linearity, and creates a discrete optimal partition of the feature space, in which the decision boundary is obtained by the influence zones of the most representative samples of the training set. Due to the large number of applications in biomedical signal processing involving pattern recognition techniques, specially voice disorders identification, we propose here the laryngeal pathology detection by means of OPF. Experiments were performed in three public datasets against SVM, and a comparison in terms of accuracy rates and execution times was also regarded.249252A.A. Spadotto, J.P. Papa, A.R. Gatto, P.C. Cola, J.C. Pereira, R.C. Guido, and A.O. Schelp, Denoising swallowing sound to improve the evaluators qualitative analysis., Computers and Electrical Engineering: Advances on Computer-based Biological Signal Processing Techniques, 34, no. 2, pp. 148-153, 2008Spadotto, A.A., Pereira, J.C., Guido, R.C., Papa, J.P., Falcão, A.X., Gatto, A.R., Cola, P.C., Shelp, A.O., Oropharyngeal dysphagia identification using wavelets and optimum path forest (2008) Proceedings of the 3th IEEE International Symposium on Communications, Control and Signal Processing, , to appearHadjitodorov, S.T., Boyanov, B., Teston, B., Laryngeal pathology detection by means of class-specific neural maps (2000) IEEE Transactions on Information Technology in Biomedicine, 4 (1), pp. 68-73Boyanov, B., Hadjitodorov, S.T., Acoustic analysis of pathological voices. a voice analysis systemfor the screening of laryngeal diseases (1997) IEEE Transactions on Engineering in Medicine and Biology Magazine, 16 (4), pp. 74-82Godino-Llorente, J.I., Vilda, P.G., Senz-Lechn, N., Blanco-Velasco, M., Craz-Roldn, F., Ferrer-Ballester, M.A., (2005) Support vector machines applied to the detection of voice disorders, 3817, pp. 219-230Perrin, E., Berger-Vachon, C., Kauffmann, I., Collet, L., (2006) Acoustical recognition of laryngeal pathology using the fundamental frequency and the first three formants of vowels, 35 (4), pp. 361-368Mezzalama, M., Prinetto, P., Morra, B., (2006) Experiments in automatic classification of laryngeal pathology, 21 (5), pp. 603-611Hadjitodorov, S.T., Ivanov, T., Boyanov, B., Analysis of dysphony using objective voice parameter (1993) Proceedings of the II Balkan Conference on Operational Research, pp. 911-917Schlotthauer, G., Torres, M.E., Jackson-Menaldi, C., Automatic diagnosis of pathological voices (2006) Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing, pp. 150-155Boser, B.E., Guyon, I.M., Vapnik, V.N., A training algorithm for-optimal margin classifiers (1992) Proc. 5th Workshop on Computational Learning Theory, pp. 144-152. , New York, NY, USA, ACM PressDuan, K., Keerthi, S.S., Which is the best multiclass svm method? an empirical study (2005) Multiple Classifier Systems, pp. 278-285Papa, J.P., Falcão, A.X., Miranda, P.A.V., Suzuki, C.T.N., Mascarenhas, N.D.A., Design of robust pattern classifiers based on optimum-path forests (2007) Mathematical Morphology and its Applications to Signal and Image Processing (ISMM), pp. 337-348. , MCT/INPEPapa, J.P., Falcão, A.X., Suzuki, C.T.N., Mascarenhas, N.D.A., A discrete approach for supervised pattern recognition (2008) 12th International Workshop on Combinatorial Image Analysis (IWCIA), 4958, pp. 136-147. , SpringerJ.A. Montoya-Zegarra, J.P. Papa, N.J. Leite, R.S. Torres, and A.X. Falcão, Rotation-invariant texture recognition, in 3rd International Symposium on Visual Computing, Lake Tahoe, Nevada, CA, USA, Nov 2007, Part II, LNCS 4842, pp. 193-204, SpringerFalcão, A.X., Stolfi, J., Lotufo, R.A., The image foresting transform: Theory, algorithms, and applications (2004) IEEE Trans. on PAMI, 26 (1), pp. 19-29. , JanAllène, C., Audibert, J.Y., Couprie, M., Cousty, J., Keriven, R., Some links between min-cuts, optimal spanning forests and watersheds (2007) Proceedings of the ISMM'08, pp. 253-264Hadjitodorov, S.T., Mitev, P., Boyanov, B., (2005) Laryngeal databases, , http://www.informatics.bangor.ac.uk/~kuncheva, Available inCohen, J., A coefficient of agreement for nominal scales (1960) Educational and Psychological Measurement, 20, pp. 37-46Chang, C.C., Lin, C.J., (2001) LIBSVM: A library for support vector machines, , http://www.csie.ntu.edu.tw/~cjlin/libsvm, Software available at ur