Abstract
In this paper, we present a comparison between a few clustering algorithms including K-means clustering (KMC), Artificial Neural Network (ANN)-based Self-Organization Map (SOM), and Fuzzy C-means (FCM) clustering for the determination of number of phonemes present in a spoken Assamese word. Here, a block is designed to determine the number of phonemes present in a particular speech dataset. The phoneme count determination technique takes some initial decision about the possible number of phonemes present in a particular word. Comparing the success rate of correct decision from the proposed clustering techniques it is observed that the SOM-based technique provides more correct decisions compared to KMC-based technique and FCM-based approach provides even better decisions than the SOM-based technique. The results show that FCM generates better performance for all the cases considered.
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References
Fukuyama Y, Sugeno M (1989) A new method of choosing the number of clusters for the fuzzy C-means method. In: Proceedings of 5th fuzzy system symposium, pp. 247–250
Granath G (1984) Application of fuzzy clustering and fuzzy classification to evaluate provenance of glacial till. J Int Assoc Math Geol 16(3):283–301
Mingoti AS, Lima OJ (2006) Comparing SOM neural network with fuzzy \(C\)-means, \(K\)-means and traditional hierarchical clustering algorithms. Eur J Oper Res 174(3):17421759
Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):547–560
Bar GD, Kuflik T, Lev D (2009) Supervised learning for automatic classification of documents using self-organizing maps. Proc IEEE Int Conf 2:1136–1139
Kohonen T (1993) Generalization of the self-organizing map. In: Proceedings of international joint conference on neural networks, pp. 457–462
Ultsch A, Vetter C (2001) Self-organizing feature maps versus statistical clustering methods: a benchmark. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.196.7322
Kohonen T, Kaski S, Lagus K, Salojarvi J, Honkela J, Paatero V, Saarela A (2000) Self organization of a massive document collection. IEEE Trans Neural Networks 11(3):574–585
Allinson N, Yin H, Allinson L, Slack J (2001) Advances in self-organising maps. Springer, London
Sarma M, Sarma KK (2012) Segmentation of assamese phonemes using SOM. Proceedings of 3rd IEEE national conference on emerging trends and applications in computer science (NCETACS). Shillong, India, pp 121–125
Iyer SN, Kandel A, Schneider M (2002) Feature-based fuzzy classification for interpretation of mammograms. Fuzzy Sets Syst. doi: 10.1016/S0165-0114(98)00175-4
Wang X, Wang Y, Wang L (2004) Improving fuzzy \(C\)-means clustering based on feature-weight learning. Pattern Recogn Lett 25(10):11231132
Sarma M, Sarma KK (2012) Recognition of assamese phonemes using three different ANN structures. Proceedings of CUBE international IT conference. Pune, India, pp 121–125
Duda OR, Hart EP, Stork GD (2000) Pattern classification, 2nd edn. Wiley-Interscience Publication, New York
Haykin S (2009) Neural network and learning machine, 3rd edn. PHI Learning Private Limited, New Delhi
Teknomo K (2003) \(K\)-means clustering tutorial. IEEE Press
Hu HY, Hwang NJ (2002) handbook of neural network signal processing. CRC Press, Boca Raton, The electrical engineering and applied signal processing
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Talukdar, P., Sarma, M., Sarma, K.K. (2015). Comparative Analysis of Neuro-Fuzzy Based Approaches for Speech Data Clustering. In: Sarma, K., Sarma, M., Sarma, M. (eds) Recent Trends in Intelligent and Emerging Systems. Signals and Communication Technology. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2407-5_11
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DOI: https://doi.org/10.1007/978-81-322-2407-5_11
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