Browsing by Author "Du Preez, J. A."

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    Adaptive estimation of speech parameters
    (IEEE, 1994) Basson, J. A. L.; Du Preez, J. A.
    Linear predictive coding (LPC), and transformations of it, is currently the most popular way of analysing speech signals. Major limitations of using a frame-based technique are that each frame is analysed in isolation of the rest while assuming the excitation source to be a white noise process. In order to reduce computation time, an all pole model is usually employed. In this project an adaptive algorithm is proposed for speech signal analysis. The algorithm is based on the recursive least squares method with a variable forgetting factor. A pole-zero model is used to estimate the anti-formants present in certain sounds (i.e. nasals and nasalized vowels). This method offers better detection of poles and zeros in stationary environments and faster tracking of pole and zero frequencies in nonstationary signals than other sequential methods. An effective input estimation algorithm eliminates the influence of pitch on the parameter estimates by assuming the input to be a white noise process or a pulse sequence.
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    Offline signature verification using the discrete Radon transform and a hidden Markov model
    (Hindawi, 2004) Coetzer, J.; Herbst, B. M.; Du Preez, J. A.
    We developed a system that automatically authenticates offline handwritten signatures using the discrete Radon transform (DRT) and a hidden Markov model (HMM). Given the robustness of our algorithm and the fact that only global features are considered, satisfactory results are obtained. Using a database of 924 signatures from 22 writers, our system achieves an equal error rate (EER) of 18% when only high-quality forgeries (skilled forgeries) are considered and an EER of 4.5% in the case of only casual forgeries. These signatures were originally captured offline. Using another database of 4800 signatures from 51 writers, our system achieves an EER of 12.2% when only skilled forgeries are considered. These signatures were originally captured online and then digitally converted into static signature images. These results compare well with the results of other algorithms that consider only global features.