Cite

Arroyave, J.R.O., Bonilla, J.F.V. and Trejos, E.D. (2012). Acoustic analysis and non linear dynamics applied to voice pathology detection: A review, Recent Patents on Signal Processing 2(2): 1-11.10.2174/2210686311202020096Search in Google Scholar

Atal, B.S. and Hanauer, S.L. (1971). Speech analysis and synthesis by linear prediction of the speech wave, The Journal of the Acoustical Society of America 50(2B): 637-655.10.1121/1.19126794106390Search in Google Scholar

Belafsky, P.C., Postma, G.N., Reulbach, T.R., Holland, B.W. and Koufman, J.A. (2002). Muscle tension dysphonia as a sign of underlying glottal insufficiency, Otolaryngology-Head and Neck Surgery 127(5): 448-451.10.1067/mhn.2002.12889412447240Search in Google Scholar

Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Vol. 1, Springer, New York, NY. Search in Google Scholar

Brinca, L.F., Batista, A.P.F., Tavares, A.I., Goncalves, I.C. and Moreno, M.L. (2014). Use of cepstral analyses for differentiating normal from dysphonic voices: A comparative study of connected speech versus sustained vowel in European Portuguese female speakers, Journal of Voice 28(3): 282-286.10.1016/j.jvoice.2013.10.00124491499Search in Google Scholar

Eadie, T.L. and Doyle, P.C. (2005). Classification of dysphonic voice: Acoustic and auditory-perceptual measures, Journal of Voice 19(1): 1-14.10.1016/j.jvoice.2004.02.00215766846Search in Google Scholar

Engel, Z.W., Klaczynski, M. and Wszolek, W. (2007). A vibroacoustic model of selected human larynx diseases, International Journal of Occupational Safety and Ergonomics 13(4): 367.10.1080/10803548.2007.1110509418082019Search in Google Scholar

Farrus, M., Hernando, J. and Ejarque, P. (2007). Jitter and shimmer measurements for speaker recognition, Annual Conference of the International Speech Communication Association (Interspeech 2007), Antwerp, Belgium, pp. 778-781. Search in Google Scholar

Fong, S., Lan, K. and Wong, R. (2013). Classifying human voices by using hybrid SFX time-series preprocessing and ensemble feature selection, BioMed Research International 2013:1-27, DOI: 10.1155/2013/720834.10.1155/2013/720834383083924288684Search in Google Scholar

Fraile, R., Saenz-Lechon, N., Godino-Llorente, J., Osma-Ruiz, V. and Fredouille, C. (2009). Automatic detection of laryngeal pathologies in records of sustained vowels by means of mel-frequency cepstral coefficient parameters and differentiation of patients by sex, Folia phoniatrica et logopaedica 61(3): 146-152.10.1159/00021995019571549Search in Google Scholar

Fujinaga, I. (1996). Adaptive Optical Music Recognition, Ph.D. thesis, McGill University, Montreal.Search in Google Scholar

Goddard, J., Schlotthauer, G., Torres, M. and Rufiner, H. (2009). Dimensionality reduction for visualization of normal and pathological speech data, Biomedical Signal Processing and Control 4(3): 194-201.10.1016/j.bspc.2009.01.001Search in Google Scholar

Godino-Llorente, J.I. and Gomez-Vilda, P. (2004). Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors, IEEE Transactions on Biomedical Engineering 51(2): 380-384.10.1109/TBME.2003.82038614765711Search in Google Scholar

Godino-Llorente, J.I., Gomez-Vilda, P. and Blanco-Velasco, M. (2006a). Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters, IEEE Transactions on Biomedical Engineering 53(10): 1943-1953.10.1109/TBME.2006.871883Search in Google Scholar

Godino-Llorente, J.I., Sáenz-Lechón, N., Osma-Ruiz, V., Aguilera-Navarro, S. and Gómez-Vilda, P. (2006b). An integrated tool for the diagnosis of voice disorders, Medical Engineering & Physics 28(3): 276-289.10.1016/j.medengphy.2005.04.014Search in Google Scholar

Hadjitodorov, S. and Mitev, P. (2002). A computer system for acoustic analysis of pathological voices and laryngeal diseases screening, Medical Engineering & Physics 24(6): 419-429.10.1016/S1350-4533(02)00031-0Search in Google Scholar

Horii, Y. (1980). Vocal shimmer in sustained phonation, Journal of Speech, Language, and Hearing Research 23(1): 202-209.10.1044/jshr.2301.2027442177Search in Google Scholar

Hu, H. and Zahorian, S.A. (2008). A neural network based nonlinear feature transformation for speech recognition, 9th Annual Conference of the International Speech Communication Association (Interspeech 2008), Brisbane, Australia, pp. 1533-1536.Search in Google Scholar

Huber, J.E., Stathopoulos, E.T., Curione, G.M., Ash, T.A. and Johnson, K. (1999). Formants of children, women, and men: The effects of vocal intensity variation, The Journal of the Acoustical Society of America 106(3): 1532-1542.10.1121/1.42715010489709Search in Google Scholar

Imai, S. (1983). Cepstral analysis synthesis on the mel frequency scale, IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP’83, Boston, MA, USA, Vol. 8, pp. 93-96.Search in Google Scholar

Jiang, J.J., Diaz, C.E. and Hanson, D.G. (1998). Finite element modeling of vocal fold vibration in normal phonation and hyperfunctional dysphonia: Implications for the pathogenesis of vocal nodules, Annals of Otology, Rhinology and Laryngology 107(7): 603-610.10.1177/0003489498107007119682857Search in Google Scholar

Joanes, D. and Gill, C. (1998). Comparing measures of sample skewness and kurtosis, Journal of the Royal Statistical Society: Series D (The Statistician) 47(1): 183-189.10.1111/1467-9884.00122Search in Google Scholar

Jothilakshmi, S. (2014). Automatic system to detect the type of voice pathology, Applied Soft Computing 21: 244-249. 10.1016/j.asoc.2014.03.036Search in Google Scholar

Lieberman, P. (1963). Some acoustic measures of the fundamental periodicity of normal and pathologic larynges, The Journal of the Acoustical Society of America 35(3): 344-353.10.1121/1.1918465Search in Google Scholar

Makhoul, J. (1975). Linear prediction: A tutorial review, Proceedings of the IEEE 63(4): 561-580.10.1109/PROC.1975.9792Search in Google Scholar

Makki, B., Hosseini, M.N. and Seyyedsalehi, S.A. (2010). An evolving neural network to perform dynamic principal component analysis, Neural Computing and Applications 19(3): 459-463.10.1007/s00521-009-0328-1Search in Google Scholar

Manfredi, C., D’Aniello, M., Bruscaglioni, P. and Ismaelli, A. (2000). A comparative analysis of fundamental frequency estimation methods with application to pathological voices, Medical Engineering & Physics 22(2): 135-147.10.1016/S1350-4533(00)00018-7Search in Google Scholar

Maran, A. (1983). Description of specific diseases of the larynx, in R. Harden and A. Marcus (Eds.), Otorhinolaryngology, Vol. 4, Springer, Dordrecht, pp. 99-104.10.1007/978-94-010-9583-9_19Search in Google Scholar

Matassini, L., Hegger, R., Kantz, H. and Manfredi, C. (2000). Analysis of vocal disorders in a feature space, Medical Engineering & Physics 22(6): 413-418.10.1016/S1350-4533(00)00048-5Search in Google Scholar

Mathieson, L., Hirani, S., Epstein, R., Baken, R., Wood, G. and Rubin, J. (2009). Laryngeal manual therapy: A preliminary study to examine its treatment effects in the management of muscle tension dysphonia, Journal of Voice 23(3): 353-366.10.1016/j.jvoice.2007.10.002Search in Google Scholar

Mehta, D.D., Deliyski, D.D., Zeitels, S.M., Quatieri, T.F. and Hillman, R.E. (2010). Voice production mechanisms following phonosurgical treatment of early glottic cancer, The Annals of Otology, Rhinology, and Laryngology 119(1): 1.10.1177/000348941011900101Search in Google Scholar

Morrison, M.D., Nichol, H. and Rammage, L.A. (1986). Diagnostic criteria in functional dysphonia, The Laryngoscope 96(1): 1-8.10.1288/00005537-198601000-00001Search in Google Scholar

Nicolosi, L., Harryman, E. and Kresheck, J. (2004). Terminology of Communication Disorders: Speech-Language- Hearing, Lippincott Williams & Wilkins, Philadelphia, PA.Search in Google Scholar

Noll, A.M. (1967). Cepstrum pitch determination, The Journal of the Acoustical Society of America 41(2): 293-309.10.1121/1.1910339Search in Google Scholar

Oja, E. (2002). Unsupervised learning in neural computation, Theoretical Computer Science 287(1): 187-207.10.1016/S0304-3975(02)00160-3Search in Google Scholar

Rabiner, L.R. and Juang, B.-H. (1993). Fundamentals of Speech Recognition, Vol. 14, PTR Prentice Hall, Englewood Cliffs, NJ.Search in Google Scholar

Rachida, D. and Amar, D. (2009). Effects of acoustic interaction between the subglottic and supraglottic cavities of the human phonatory system, Canadian Acoustics 37(2): 37-43.Search in Google Scholar

Roy, N. (2003). Functional dysphonia, Current Opinion in Otolaryngology & Head and Neck Surgery 11(3): 144-148. 10.1097/00020840-200306000-0000212923352Search in Google Scholar

Saenz-Lechon, N., Godino-Llorente, J.I., Osma-Ruiz, V., Blanco-Velasco, M. and Cruz-Roldan, F. (2006). Automatic assessment of voice quality according to the GRBAS scale, 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS’06, New York, NY, USA, pp. 2478-2481.Search in Google Scholar

Saldanha, J.C., Ananthakrishna, T. and Pinto, R. (2014). Vocal fold pathology assessment using mel-frequency cepstral coefficients and linear predictive cepstral coefficients features, Journal of Medical Imaging and Health Informatics 4(2): 168-173.10.1166/jmihi.2014.1253Search in Google Scholar

Schölkopf, B., Smola, A. and Müller, K.-R. (1999). Kernel principal component analysis, in B. Schölkopf, C.J.C.Search in Google Scholar

Burges and A.J. Smola (Eds.), Advances in Kernel Methods-Support Vector Learning, MIT Press, Cambridge, MA.Search in Google Scholar

Scholz, M., Fraunholz, M. and Selbig, J. (2008). Nonlinear principal component analysis: Neural network models and applications, in A.N. Gorban et al. (Eds.), Principal Manifolds for Data Visualization and Dimension Reduction, Springer, Berlin/Heidelberg, pp. 44-67.10.1007/978-3-540-73750-6_2Search in Google Scholar

Scholz, M. and Vigário, R. (2002). Nonlinear PCA: A new hierarchical approach, 10th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, pp. 439-444.Search in Google Scholar

Skalski, A., Zielinski, T. and Deliyski, D. (2008). Analysis of vocal folds movement in high speed videoendoscopy based on level set segmentation and image registration, International Conference on Signals and Electronic Systems, ICSES’ 08, Kraków, Poland, pp. 223-226.Search in Google Scholar

Steinecke, I. and Herzel, H. (1995). Bifurcations in an asymmetric vocal-fold model, The Journal of the Acoustical Society of America 97(3): 1874-1884.10.1121/1.4120617699169Search in Google Scholar

Sulica, L. and Blitzer, A. (Eds.) (2006). Vocal Fold Paralysis, Springer, Berlin/Heidelberg.10.1007/3-540-32504-2Search in Google Scholar

Tadeusiewicz, R., Korbicz, J., Rutkowski, L. and Duch, W. (Eds.) (2013). Neural Networks in Biomedical Engineering, Inżynieria biomedyczna. Podstawy i zastosowania, Vol. 9, Akademicka Oficyna Wydawnicza EXIT, Warsaw, (in Polish).Search in Google Scholar

Tsanas, A. (2013). Acoustic analysis toolkit for biomedical speech signal processing: Concepts and algorithms, Models and Analysis of Vocal Emissions for Biomedical Applications 2: 37-40.Search in Google Scholar

Umapathy, K., Krishnan, S., Parsa, V. and Jamieson, D.G. (2005). Discrimination of pathological voices using a time-frequency approach, IEEE Transactions on Biomedical Engineering 52(3): 421-430.10.1109/TBME.2004.84296215759572Search in Google Scholar

Wang, Q. (2012). Kernel principal component analysis and its applications in face recognition and active shape models, ARXIV 1207.3538.Search in Google Scholar

Wong, D., Markel, J. and Gray Jr, A. (1979). Least squares glottal inverse filtering from the acoustic speech waveform, IEEE Transactions on Acoustics, Speech and Signal Processing 27(4): 350-355.10.1109/TASSP.1979.1163260Search in Google Scholar

Yumoto, E., Gould, W.J. and Baer, T. (1982). Harmonics-to-noise ratio as an index of the degree of hoarseness, The Journal of the Acoustical Society of America 71(6): 1544-1550.10.1121/1.3878087108029Search in Google Scholar

Zahorian, S. and Hu, H. (2011). Nonlinear Dimensionality Reduction Methods for Use with Automatic Speech Recognition, Vol. 06, Speech Technologies Source: InTech, Rijeka. Search in Google Scholar

eISSN:
2083-8492
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Mathematics, Applied Mathematics