Abstract
In this paper, we address the problem of learning a classifier for the classification of spoken character. We present a solution based on Group Method of Data Handling (GMDH) learning paradigm for the development of a robust abductive network classifier. We improve the reliability of the classification process by introducing the concept of multiple abductive network classifier system. We evaluate the performance of the proposed classifier using three different speech datasets including spoken Arabic digit, spoken English letter, and spoken Pashto digit. The performance of the proposed classifier surpasses that reported in the literature for other classification techniques on the same speech datasets.
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References
Abductory Information Modeler (AIM)—AbTech Corporation (1990). AIM User’s Manual. Charlottesville, VA.
Adam, T., & Salam, M. (2012). Spoken english alphabet recognition with mel frequency cepstral coefficients and back propagation neural networks. International Journal of Computer Applications, 42(12), 21–27.
Ali, Z., Abbas, A., Thasleema, T. M., Uddin, B., Raaz, T., & Abid, S. A. (2015). Database development and automatic speech recognition of isolated pashto spoken digits using mfcc and k-nn. International Journal of Speech Technology, 18(2), 271–275.
Ananthi, S., & Dhanalakshmi, P. (2015). SVM and HMM modeling techniques for speech recognition using LPCC and MFCC features (pp. 519–526). New York: Springer International Publishing.
Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3), 1–27.
Dietterich, T. G., & Bakiri, G. (1995). Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2, 263–286.
Farlow, S. (1984). The GMDH algorithm. In S. Farlow (Ed.), Self-organising methods in modelling: GMDH type algorithms. New York: Marcel-Dekker.
Hammami, N., Bedda, M., & Farah, N. (2013). Probabilistic classification based on gaussian copula for speech recognition: Application to spoken arabic digits, In Proceedings of the Conference on Signal Processing: Algorithms, Architectures, Arrangements, and Applications, (pp. 312–317).
Lanjewar, R., Mathurkar, S., & Patel, N. (2015). Implementation and comparison of speech emotion recognition system using gaussian mixture model (gmm) and k- nearest neighbor (k-nn) techniques. Procedia Computer Science, 49, 50–57.
Lawal, I.A., Abdel-Aal, R.E., & Mahmoud, S.A. (2010). Recognition of handwritten arabic (indian) numerals using freeman’s chain codes and abductive network classifiers, In Proceedings of the International Conference on Pattern Recognition, (pp. 1884–1887).
Lichman, M. (2013). UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences, URL http://archive.ics.uci.edu/ml.
Muhammad, G., Alotaibi, Y.A., & Huda, M.N. (2009). Automatic speech recognition for bangla digits, In Proceedings of the International Conference on Computers and Information Technology, (pp. 379–383).
Nisar, S., Shahzad, I., Khan, M.A., & Tariq, M. (2017). Pashto spoken digits recognition using spectral and prosodic based feature extraction, In Proceedings of the International Conference on Advanced Computational Intelligence, (pp. 74–78).
Onwubolu, G. (2015). GMDH-methodology and implementation in MATLAB. London, UK: Imperial College Press.
Padmanabhan, J., & Johnson Premkumar, M. (2015). Machine learning in automatic speech recognition: A survey. IETE Technical Review, 32(4), 240–251.
Pohjalainen, J., Rsnen, O., & Kadioglu, S. (2015). Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Computer Speech and Language, 29(1), 145–171.
Sarma, M., & Sarma, K. K. (2015). Speech recognition in Indian languages—A survey (pp. 173–187). India: Springer.
Silva, D. F., de Souza, V. M. A., Batista, G., & Giusti, R. (2012). Spoken digit recognition in portuguese using line spectral frequencies (pp. 241–250)., Lecture Notes in Computer Science, Vol. 7637, Berlin: Springer.
Wiqas, G., & Navdeep, S. (2012). Literature review on automatic speech recognition. International Journal of Computer Applications, 8(41), 42–50.
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Lawal, I.A. Spoken character classification using abductive network. Int J Speech Technol 20, 881–890 (2017). https://doi.org/10.1007/s10772-017-9460-y
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DOI: https://doi.org/10.1007/s10772-017-9460-y