AN ADAPTIVE CLASSIFIER DESIGN FOR ACCURATE SPEECH DATA CLASSIFICATION

Omid Dehzangi, Ehsan Younessian, Fariborz Hosseini Fard

2009

Abstract

In this paper, an adaptive approach to designing accurate classifiers using Nearest Neighbor (NN) and Linear Discriminant Analysis (LDA) is proposed. A novel NN rule with an adaptive distance measure is proposed to classify input patterns. An iterative learning algorithm is employed to incorporate a local weight to the Euclidean distance measure that attempts to minimize the number of misclassified patterns in the training set. In case of data sets with highly overlapped classes, this may cause the classifier to increase its complexity and overfit. As a solution, LDA is considered as a popular feature extraction technique that aims at creating a feature space that best discriminates the data distributions and reduces overlaps between different classes of data. In this paper, an improved variation of LDA (im-LDA) is investigated which aims to moderate the effect of outlier classes. The proposed classifier design is evaluated by 6 standard data sets from UCI ML repository and eventually by TIMIT data set for framewise classification of speech data. The results show the effectiveness of the designed classifier using im-LDA with the proposed ad-NN method.

References

  1. Cover, T.M., Hart, P.E., 1967. Nearest Neighbor Pattern Classification. IEEE Transaction on Information Theory 13, 21-27.
  2. Friedman, J., 1994. Flexible metric nearest neighbor classification. Technical Report 113, Stanford University Statistics Department.
  3. Hastie, T., Tibshirani, R., 1996. Discriminant adaptive nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18: 607- 615.
  4. Domeniconi, C., Peng, J., Gunopulos, D., 2002. Locally adaptive metric nearest neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24: 1281-1285.
  5. Wang, J., Neskovic, P., Cooper, L.N., 2007. Improving nearest neighbor rule with a simple adaptive distance measure. Pattern Recogition Letters, 28: 207-213.
  6. Fisher, R.A., 1936. The Use of Multiple Measurements in Taxonomic Problems, Annals of Eugenics, 7:179-188.
  7. Duda, R.O., Hart, P.E., Stork, D., 2001. Pattern Classification 2nd Edition. Wiley, New York.
  8. Loog, M., Duin, R.P.W., Haeb-Umbach, R., 2001. Multiclass linear dimension reduction by weighted pairwise fisher criteria, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23: 762-766.
  9. Jarchi, D., Boostani, R., 2006. A New Weighted LDA Method in Comparison to Some Versions of LDA, Transaction on Engineering and Computational Technology, 18: 18-45.
  10. Garofolo, J.S., 1988. Getting started with the DARPA TIMIT CD-ROM: An acoustic phonetic continuous speech database, National Institute of Standards and Technology (NIST), Gaithersburgh, MD.
  11. Merz, C.J., Murphy, P.M., 1996. UCIRepository of Machine Learning Databases. Irvine, CA: University of California Irvine, Department of information and Computer Science. Internet: http://www.ics.uci.edu/mlearn/MLRepository.html Schuster, M., Paliwal, K.K., 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45: 2673-2681.
  12. Graves, A., Schmidhuber, J., 2005. Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures. International Joint Conference on Neural Networks.
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Paper Citation


in Harvard Style

Dehzangi O., Younessian E. and Hosseini Fard F. (2009). AN ADAPTIVE CLASSIFIER DESIGN FOR ACCURATE SPEECH DATA CLASSIFICATION . In Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-674-000-9, pages 67-71. DOI: 10.5220/0002206200670071


in Bibtex Style

@conference{icinco09,
author={Omid Dehzangi and Ehsan Younessian and Fariborz Hosseini Fard},
title={AN ADAPTIVE CLASSIFIER DESIGN FOR ACCURATE SPEECH DATA CLASSIFICATION},
booktitle={Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2009},
pages={67-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002206200670071},
isbn={978-989-674-000-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - AN ADAPTIVE CLASSIFIER DESIGN FOR ACCURATE SPEECH DATA CLASSIFICATION
SN - 978-989-674-000-9
AU - Dehzangi O.
AU - Younessian E.
AU - Hosseini Fard F.
PY - 2009
SP - 67
EP - 71
DO - 10.5220/0002206200670071