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Recognition of Consonant-Vowel (CV) Units of Speech in a Broadcast News Corpus Using Support Vector Machines

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Pattern Recognition with Support Vector Machines (SVM 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2388))

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Abstract

This paper addresses the issues in recognition of the large number of subword units of speech using support vector machines (SVMs). In conventional approaches for multi-class pattern recognition using SVMs, learning involves discrimination of each class against all the other classes. We propose a close-class-set discrimination method suitable for large-class-set pattern recognition problems. In the proposed method, learning involves discrimination of each class against a subset of classes confusable with it and included in its close-class-set. We study the effectiveness of the proposed method in reducing the complexity of multi-class pattern recognition systems based on the one-against-the rest and one-against-one approaches. We discuss the effects of symmetry and uniformity in size of the close-class-sets on the performance for these approaches. We present our studies on recognition of 86 frequently occurring Consonant-Vowel units in a continuous speech database of broadcast news.

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References

  1. E. L. Allwein, R. E. Schapire, and Y. Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research, 1:113–141, December 2000.

    Google Scholar 

  2. P. Beyerlein, X. Aubert, R. Haeb-Umbach, M. Harris, D. Klakow, A. Wendemuth, S. Molau, H. Ney, M. Pitz, and A. Sixtus. Large vocabulary continuous speech recognition of broadcast news-The Philips/RWTH approach. Speech Communication, 2002.

    Google Scholar 

  3. P. Clarkson and P. J. Moreno. On the use of support vector machines for phonetic classification. In Proceedings of ICASSP, pages 585–588, March 1999.

    Google Scholar 

  4. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley & Sons, Inc., 2001.

    Google Scholar 

  5. U. Kreßel. Pairwise classification and support vector machines. In B. Scholköpf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods-Support Vector Learning, pages 255–268. The MIT Press, 1999.

    Google Scholar 

  6. A. Ganapathiraju. Support Vector Machines for Speech Recognition. PhD thesis, Mississippi State University, Mississippi, 2002.

    Google Scholar 

  7. A. Ganapathiraju, J. Hamaker, J. Picone, M. Ordowski, and G. R. Doddington. Syllable-based large vocabulary continuous speech recognition. IEEE Transactions on Speech and Audio Processing, 9(4):358–366, May 2001.

    Google Scholar 

  8. S. Haykin. Neural Networks-A Comprehensive Foundation. Prentice Hall, 1999.

    Google Scholar 

  9. S. Katagiri, editor. Handbook of Neural Networks for Speech Processing. Artech House, 2000.

    Google Scholar 

  10. J. Platt, N. Cristianini, and J. Shawe-Taylor. Large margin DAGs for multiclass classification. In S. A. Solla, T. K. Leen, and K-R. Muller, editors, Advances in Neural Information Processing Systems, 12, pages 547–553. The MIT Press, 2000.

    Google Scholar 

  11. L. R. Rabiner and B. H. Juang. Fundamentals of Speech Recognition. Prentice Hall, 1993.

    Google Scholar 

  12. C. Chandra Sekhar, Kazuya Takeda, and Fumitada Itakura. Close-class-set discrimination method for large-class-set pattern recognition recognition using support vector machines. In International Joint Conference on Neural Networks, May 2002.

    Google Scholar 

  13. H. Shimodaira, K. Noma, M. Nakai, and S. Sagayama. Support vector machine with dynamic time-alignment kernel for speech recognition. In Proceedings of Eu-rospeech, pages 1841–1844, September 2001.

    Google Scholar 

  14. N. Smith and M. Gales. Speech recognition using SVMs. In Advances in Neural Information Processing Systems, 2001.

    Google Scholar 

  15. J. Weston and C. Watkins. Multi-class support vector machines. Technical Report CSD-TR-98-04, Royal Holloway, University of London, May 1998.

    Google Scholar 

  16. B. Yegnanarayana. Artificial Neural Networks. Prentice Hall of India, 1999.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Center for Integrated Acoustic Information Research Dept. of Information Electronics, Nagoya University, Nagoya, Japan

    C. Chandra Sekhar, Kazuya Takeda & Fumitada Itakura

Authors
  1. C. Chandra Sekhar
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  2. Kazuya Takeda
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  3. Fumitada Itakura
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Korea University, Anam-dong, Seongbuk-ku, Seoul, 136-701, Korea

    Seong-Whan Lee

  2. Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, Via Dodecaneso 35, 16146, Genova, Italy

    Alessandro Verri

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© 2002 Springer-Verlag Berlin Heidelberg

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Sekhar, C.C., Takeda, K., Itakura, F. (2002). Recognition of Consonant-Vowel (CV) Units of Speech in a Broadcast News Corpus Using Support Vector Machines. In: Lee, SW., Verri, A. (eds) Pattern Recognition with Support Vector Machines. SVM 2002. Lecture Notes in Computer Science, vol 2388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45665-1_14

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  • DOI: https://doi.org/10.1007/3-540-45665-1_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44016-1

  • Online ISBN: 978-3-540-45665-0

  • eBook Packages: Springer Book Archive

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