Publications, theses and other documents


    Publications

    N.D. Smith and M.J.F. Gales. Using SVMs and Discriminative Models for Speech Recognition. In Proceedings, pages 77-80. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002.
    N. Smith and M. Gales. Speech Recognition using SVMs. In T.G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14 , pages 1197-1204. The MIT Press, 2002.
    N. Smith and M. Niranjan. Data-Dependent Kernels in SVM Classification of Speech Patterns. In Proceedings . International Conference on Spoken Language Processing, 2000.
    Q. Huo, N. Smith and B. Ma. Efficient ML Training of CDHMM Parameters Based on Prior Evolution, Posterior Intervention and Feedback. In Proceedings , pages 1001-1004. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2000.

    Theses

    N.D. Smith. Using Augmented Statistical Models and Score Spaces for Classification. PhD thesis, Cambridge University Engineering Department, September 2003.
    N. Smith. Support Vector Machines applied to Speech Pattern Classification, MPhil thesis, Cambridge University Engineering Department, August 1998.

      Warning: From recent investigations, it appears that all the SVM experiments in the above MPhil thesis were conducted with the SVM bias fixed at zero (i.e. if the SVM classifier is a linear discriminant (w,b) in a kernel-induced feature space, where `w' is the weight vector of the linear discriminant and `b' is its bias, then b=0). The thesis gives the impression that the biases for all SVM classifiers were free variables (this was in fact the original intention). However the fact that the biases for these classifiers were not free variables seriously limits the modelling ability of these SVM classifiers. It is important for the reader to take this into account when reading the thesis and making comparisons. Fuller investigations are required to check whether any experiments were performed where nonzero biases were allowed.

    Technical Reports

    N.D. Smith and M.J.F. Gales. Using SVMs to Classify Variable Length Speech Patterns. Technical Report CUED/F-INFENG/TR.412, Cambridge University Engineering Department, April 2002 (updated June 2002).

      Warning: The experiments in the above report CUED/F-INFENG/TR.412 were conducted over a number of months during which the software was gradually made more robust. The experiments were therefore performed with slightly different versions of software. It is important for the reader to take this into account when reading the report and making comparisons.

    N. Smith, M. Gales, and M. Niranjan. Data-Dependent Kernels in SVM Classification of Speech Patterns. Technical Report CUED/F-INFENG/TR.387, Cambridge University Engineering Department, April 2001 (updated April 2002).


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    Last updated: 8th January 2005