Voice pathology assessment based on automatic speech recognition using Amazigh digits
Pages 100 - 105
Abstract
In the past few years, research on automatic systems to assess voice disorders has received appreciable attention due to its objectivity and noninvasive nature. The work presented in this paper aims to build an automatic speech recognition system based on Sphinx4 that permit to detect the people who have disorders voices. This research project is carried out using Amazigh language in order to differentiate the normal and pathological voices. The performance in our system was measured using combinations of HMMs 5-states with 8 Gaussian mixture distributions. Our obtained results are very satisfying, where a vast difference between normal and pathological speakers accuracy.
References
[1]
Azmi, M. M., Tolba, H., Mahdy, S., & Fashal, M. Syllable-based automatic Arabic speech recognition. In: Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation. World Scientific and Engineering Academy and Society, p. 246--250, 2008.
[2]
Satori, H., Hiyassat, H., Haiti, M. and Chenfour, N. Investigation Arabic Speech Recognition Using CMU Sphinx System. International Arab Journal of Information Technology (IAJIT), vol. 6, no. 2, 2009.
[3]
Hamidi, M., Satori, H. and Satori, K. Implementing a voice interface in VOIP network with IVR server using Amazigh digits. The International Journal of Multidisciplinary Sciences -Issue 2, vol. 2, pp. 38--43, 2016.
[4]
Manfredi, C. and Kob, M. New trends in voice pathology detection and classification. Biomedical Signal Processing and Control, vol. 3, no. 4, pp. 171--172, 2009.
[5]
Satori, H., Zealouk, O., Satori, K. and El Haoussi, F. Voice comparison between smokers and non-smokers using HMM speech recognition system. International Journal of Speech Technology, vol. 20, no. 4, pp. 771--777, 2017.
[6]
Zealouk, O., Satori, H., Hamidi, M., Laaidi, N. and Satori, K. Vocal parameters analysis of smoker using Amazigh language. International Journal of Speech Technology, vol. 21, no. 1, pp. 85--91, 2018.
[7]
Maier, A., Haderlein, T., Eysholdt, U., Rosanowski, F., Batliner, A., Schuster, M. and Nöth, E. PEAKS-A system for the automatic evaluation of voice and speech disorders. Speech Communication, vol. 51, no. 5, pp. 425--437, 2009.
[8]
Muhammad, G., Mesallam, T. A., Malki, K. H., Farahat, M., Alsulaiman, M. and Bukhari, M. Formant analysis in dysphonic patients and automatic Arabic digit speech recognition. Biomedical engineering online, vol. 10, no. 1, pp. 41, 2011.
[9]
Godino-Llorente, J. I., Gomez-Vilda, P. and Blanco-Velasco, M. Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters. IEEE transactions on biomedical engineering, vol. 53, no. 10, pp. 1943--1953, 2006.
[10]
Wiśmewski, M., Kumszyk-Jóźkowiak, W., Smołka, E. and Suszyński, W. Automatic detection of disorders in a continuous speech with the hidden Markov models approach. In Computer Recognition Systems 2, Springer, Berlin, Heidelberg, p. 445--453, 2007.
[11]
Muhammad., G, Alsulaiman, M., Ali, Z., Mesallam, T. A., Farahat, M., Malki, K. H,... and Bencherif, M. A. Voice pathology detection using interlaced derivative pattern on glottal source excitation. Biomedical Signal Processing and Control, vol. 31, p. 156--164, 2017.
[12]
Woldert-Jokisz, B. Saarbruecken voice database, 2007.
[13]
Ali, Z., Alsulaiman, M., Muhammad, G., Elamvazuthi, I., Al-nasheri, A., Mesallam, T. A.,... and Malki, K. H. Intra-and Inter-database Study for Arabic, English, and German Databases: Do Conventional Speech Features Detect Voice Pathology?. Journal of Voice, vol. 31, no. 3, pp. 386.e1--386.e8, 2017.
[14]
Gaikwad, S. K., Gawali, B. W. and Yannawar, P. A review on speech recognition technique. International Journal of Computer Applications, vol. 10, no. 3, pp. 16--24, 2010.
[15]
Huang, X., Acero, A., Hon, H. W. and Reddy, R. 2001. Spoken language processing: A guide to theory, algorithm, and system development. Upper Saddle River: Prentice hall PTR, vol. 1, 2001.
[16]
Klevans, R. L. and Rodman, R. D. Voice recognition. Artech House, Inc., 1997.
[17]
Desai, N., Dhameliya, K. and Desai, V. Feature extraction and classification techniques for speech recognition: A review. International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 12, pp. 367--371, 2013.
[18]
Boukous, A. Société, langues et cultures au Maroc: Enjeux symboliques (No. 8). Faculté des lettres et des sciences humaines-Rabat, 1995.
[19]
Ouakrim, O. 1995. Fonética y fonología del Bereber, vol 3. Servei de Publicacions de la Universitat Autònoma de Barcelona, 1995.
[20]
Chaker, S. Textes en linguistique berbère: introduction au domaine berbère. Paris: Ed. du C.N.R.S, 1984.
[21]
Fadoua, A. A. and Siham, B. Natural language processing for Amazigh language: Challenges and future directions. Language Technology for Normalisation of Less-Resourced Languages, vol. 19, 2012.
[22]
Ridouane, R. Suites de consonnes en berbère: phonétique et phonologie (Doctoral dissertation, Université de la Sorbonne nouvelle-Paris III), 2003.
[23]
Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Liu, X.... and Valtchev, V. The HTK book. Cambridge university engineering department, vol. 3, no. 175, 2002.
[24]
Pawar, G. S. and Morade, S. S. Realization of Hidden Markov Model for English Digit Recognition. International Journal of Computer Applications, vol. 98, no. 17, 2014.
[25]
Carnegie Mellon University. Sphinx-4. Available http://cmusphinx.sourceforge.net
[26]
Satori, H., Harti, M. and Chenfour, N. Arabic speech recognition system using cmu-sphinx4. arXiv preprint arXiv:0704.2201, 2007.
[27]
Varela, A., Cuayáhuitl, H. and Nolazco-Flores, J. A. Creating a Mexican Spanish version of the CMU Sphinx-III speech recognition system. In Iberoamerican Congress on Pattern Recognition, pp. 251--258, 2003.
[28]
Ali, Z., Alsulaiman, M., Elamvazuthi, I., Muhammad, G., Mesallam, T. A., Farahat, M. and Malki, K. H. Voice pathology detection based on the modified voice contour and SVM. Biologically Inspired Cognitive Architectures, vol. 15, pp. 10--18, 2016.
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Published: 18 October 2018
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