Identification of Question and Non-Question Segments in Arabic Monologues Using Prosodic Features: Novel Type-2 Fuzzy Logic and Sensitivity-Based Linear Learning Approaches


In this paper, we extend our previous study of addressing the important problem of automatically identifying question and non-question segments in Arabic monologues using prosodic features. We propose here two novel classification approaches to this problem: one based on the use of the powerful type-2 fuzzy logic systems (type-2 FLS) and the other on the use of the discriminative sensitivity-based linear learning method (SBLLM). The use of prosodic features has been used in a plethora of practical applications, including speech-related applications, such as speaker and word recognition, emotion and accent identification, topic and sentence segmentation, and text-to-speech applications. In this paper, we continue to specifically focus on the Arabic language, as other languages have received a lot of attention in this regard. Moreover, we aim to improve the performance of our previously-used techniques, of which the support vector machine (SVM) method was the best performing, by applying the two above-mentioned powerful classification approaches. The recorded continuous speech is first segmented into sentences using both energy and time duration parameters. The prosodic features are then extracted from each sentence and fed into each of the two proposed classifiers so as to classify each sentence as a Question or a Non-Question sentence. Our extensive simulation work, based on a moderately-sized database, showed the two proposed classifiers outperform SVM in all of the experiments carried out, with the type-2 FLS classifier consistently exhibiting the best performance, because of its ability to handle all forms of uncertainties.

Share and Cite:

S. Olatunji, L. Cheded, W. Al-Khatib and O. Khan, "Identification of Question and Non-Question Segments in Arabic Monologues Using Prosodic Features: Novel Type-2 Fuzzy Logic and Sensitivity-Based Linear Learning Approaches," Journal of Intelligent Learning Systems and Applications, Vol. 5 No. 3, 2013, pp. 165-175. doi: 10.4236/jilsa.2013.53018.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] I. H. Witten and E. Frank, “Data Mining, Practical Machine Learning Tools and Techniques,” 2nd Edition, Elsevier, San Francisco, 2005.
[2] A. Selamat, S. O. Olatunji and A. A. Abdul Raheem, “A Hybrid Model through the Fusion of Type-2 Fuzzy Logic Systems and Sensitivity-Based Linear Learning Method for Modeling PVT Properties of Crude Oil Systems,” Advances in Fuzzy Systems, Vol. 2012, 2012, Article ID: 359429. doi:10.1155/2012/359429
[4] E. Shriberg, et al., “Can Prosody Aid the Automatic Classification of Dialog Acts in Conversational Speech?” Language and Speech Special Issue on Prosody and Conversation, Vol. 41, 1998, pp. 443-492.
[5] R. Fernandez and R. W. Picard, “Dialog Act Classifica tion from Prosodic Features Using Support Vector Ma chines,” Proceedings of Speech Prosody, Aix-en-Pro vence, 11-13 April 2002.
[6] M. Swerts, “Prosodic Features at Discourse Boundaries of Different Strengths,” Journal of the Acoustical Society of America Phonetics, Vol. 101, No. 1, 1997, pp. 514-521.
[7] S. Pfeiffer, “Pause Concepts for Audio Segmentation at Different Semantic Levels,” Proceedings of the ninth ACM International Conference on Multimedia, Ottawa, 20 September 2001-5 October 2001, pp. 187-193.
[8] C. M. Lee and S. S. Narayanan, “Toward Detecting Emotions in Spoken Dialogs,” IEEE Transactions on Speech and Audio Processing, Vol. 13, No. 2, 2005, pp. 293-303.
[9] J. M. Mendel, “Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions,” Prentice Hall, Upper Saddle River, 2001.
[10] J. M. Mendel and R. I. B. John, “Type-2 Fuzzy Sets Made Simple,” IEEE Transactions on Fuzzy Systems, Vol. 10, No. 2, 2002, pp. 117-127. doi:10.1109/91.995115
[11] J. M. Mendel, “Fuzzy Sets for Words: A New Beginning,” The 12th IEEE International Conference on Fuzzy Systems, Los Angeles, 25-28 May 2003, pp. 37-42.
[12] Q. Liang, N. N. Karnik and J. M. Mendel, “Connection Admission Control in ATM Networks Using Survey Based Type-2 Fuzzy Logic Systems: Applications and Reviews,” IEEE Transactions on Systems, Man and Cybernetics: Part C, Vol. 30, No. 3, 2000, pp. 329-339. doi:10.1109/5326.885114
[13] M. H. F. Zarandi, et al., “A Type-2 Fuzzy Rule-Based Expert System Model for Stock Price Analysis,” Expert Systems with Applications, Vol. 36, No. 1, 2009, pp. 139-154.
[14] W. Dongrui and J. M. Mendel, “A Vector Similarity Measure for Linguistic Approximation: Interval Type-2 and Type-1 Fuzzy Sets,” Information Sciences, Vol. 178, No. 2, 2008, pp. 381-402. doi:10.1016/j.ins.2007.04.014
[15] E. Castillo, et al., “A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis,” Journal of Machine Learning Research, Vol. 7, 2006, pp. 1159-1182.
[16] A. S. Castillo, et al., “A General Method for Local Sensitivity Analysis with Application to Regression Models and Other Optimization Problems,” Technometrics, Vol. 46, No. 4, 2004, pp. 430-445. doi:10.1198/004017004000000509
[17] O. Khan, “Detection of Questions in Arabic Audio Monologues Using Prosodic Features,” The 9th International Symposium on Multimedia, Taichung, 10-12 December 2007, pp. 29-36.
[18] E. Cox, “Adaptive Fuzzy Systems,” IEEE Spectrum, Vol. 30, No. 2, 1993, pp. 27-31. doi:10.1109/6.208359
[19] D. Dubois and H. Prade, “Fuzzy Sets and Systems: Theory and Applications,” Academic Press, New York, 1982
[20] L. A. Zadeh, “The Concept of a Linguistic Variable and its Application to Approximate Reasoning—I,” Information Sciences, Vol. 8, No. 3, 1975, pp. 199-249. doi:10.1016/0020-0255(75)90036-5
[21] S. S. Lee and K. H. Lee, “A Ranking Method for Type-2 Fuzzy Values,” Journal of Korea Fuzzy and Intelligent Systems Society, Vol. 12, No. 4, 2002, pp. 341-346. doi:10.5391/JKIIS.2002.12.4.341
[22] Q. Liang and J. M. Mendel, “Interval Type-2 Fuzzy Logic Systems: Theory and Design,” IEEE Transactions on Fuzzy Systems, Vol. 8, No. 5, 2000, pp. 535-550.
[23] N. N. Karnik, J. M. Mendel and Q. Liang, “Type-2 Fuzzy Logic Systems,” IEEE Transactions on Fuzzy Systems, Vol. 7, No. 6, 1999, pp. 643-658. doi:10.1109/91.811231
[24] Q. Liang and J. M. Mendel, “Equalization of Non-linear Time-Varying Channels Using Type-2 Fuzzy Adaptive Filters,” IEEE Transactions on Fuzzy Systems, Vol. 8, No. 5, 2000, pp. 551-563. doi:10.1109/91.873578
[25] Q. Liang, and J. M. Mendel, “Overcoming Time-Varying Co-channel Interference Using Type-2 Fuzzy Adaptive Filters,” IEEE Transactions on Circuits and Systems, Vol. 47, No. 12, 2000, pp. 1419-1428. doi:10.1109/82.899635
[26] S. O. Olatunji, A. Selamat and A. Abdulraheem, “Model ing the Permeability of Carbonate Reservoir Using Type-2 Fuzzy Logic Systems,” Computers in Industry, Vol. 62, No. 2, 2011, pp. 147-163. doi:10.1016/j.compind.2010.10.008
[27] S. O. Olatunji, A. Selamat and A. A. A. Raheem, “Pre dicting Correlations Properties of Crude Oil Systems Us ing Type-2 Fuzzy Logic Systems,” Expert Systems with Applications, Vol. 38, No. 9, 2011, pp. 10911-10922. doi:10.1016/j.eswa.2011.02.132
[28] H. Nguyen, V. Kreinovich and Q. Zuo, “Interval Valued Degrees of Belief: Applications of Interval Computations to Expert Systems and Intelligent Control,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 5, No. 3, 1997, pp. 317-358. doi:10.1142/S0218488597000257
[29] F. Liu, “An Efficient Centroid Type-Reduction Strategy for General Type-2 Fuzzy Logic System,” Information Sciences, Vol. 178, No. 9, 2008, pp. 2224-2236. doi:10.1016/j.ins.2007.11.014
[30] J. M. Mendel, “Computing with Words and Its Relationships with Fuzzistics,” Information Sciences, Vol. 177, No. 4, 2007, pp. 988-1006. doi:10.1016/j.ins.2006.06.008
[31] J. M. Mendel and H. Wu, “New Results about the Centroid of an Interval Type-2 Fuzzy Set, Including the Centroid of a Fuzzy Granule,” Information Sciences, Vol. 177, No. 2, 2007, pp. 360-377. doi:10.1016/j.ins.2006.03.003
[32] D. Wu and J. M. Mendel, “Uncertainty Measures for Interval Type-2 Fuzzy Sets,” Information Sciences, Vol. 177, No. 23, 2007, pp. 5378-5393. doi:10.1016/j.ins.2007.07.012
[33] A. C. Castillo, J. M. Guti′errez and R. E. Pruneda, “Wor king with Differential, Functional and Difference Equa tions Using Functional Networks,” Applied Mathematical Modelling, Vol. 23, No. 2, 1999, pp. 89-107. doi:10.1016/S0307-904X(98)10074-4
[34] A. S. Castillo, J. M. Guti′errez and A. Hadi, “Sensitivity Analysis in Discrete Bayesian Networks,” IEEE Transac tions on Systems, Man and Cybernetics, Vol. 26, no. 7, 1997, pp. 412-423. doi:10.1109/3468.594909
[35] S. O. Olatunji, et al., “Modeling the Correlations of Crude Oil Properties Based on Sensitivity Based Linear Learning Method,” Engineering Applications of Artificial Intelligence, Vol. 24, No. 4, 2011, pp. 686-696. doi:10.1016/j.engappai.2010.10.007
[36] J. M. Mendel and R. I. B. John, “Type-2 Fuzzy Sets Made Simple,” IEEE Transactions on Fuzzy Systems, Vol. 10, No. 2, 2002, pp. 117-127. doi:10.1109/91.995115
[37] O. Castillo, “Type-2 Fuzzy Logic in Intelligent Control Applications, Studies in Fuzziness and Soft Computing Series, Vol. 272, Springer, Berlin, 2012.
[38] N. N. Karnik and J. M. Mendel, “Centroid of a Type-2 Fuzzy Set,” Information Sciences, Vol. 132, No. 1-4, 2001, pp. 195-220. doi:10.1016/S0020-0255(01)00069-X
[39] K. Omair, G. A.-K. Wasfi and C. Lahouari, “A Preliminary Study of Prosody-Based Detection of Questions in Arabic Speech Monologues,” The Arabian Journal for Science and Engineering, Vol. 35, No. 2, 2010, pp. 167-181.

Copyright © 2022 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.