Browsing by Author "Supreeth Prajwal, S.S."

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    Improved Vowel Onset and offset points detection using bessel features
    (Institute of Electrical and Electronics Engineers Inc., 2014) Sarma, B.D.; Supreeth Prajwal, S.S.; Prasanna, S.R.M.
    This work presents a method for improving accuracy of Vowel Onset Point (VOP) and Vowel End Point (VEP) detection in continuous speech. VOP and VEP are the instants at which the onset and offset of vowel takes place, respectively, during speech production. Speech signal is represented using Bessel functions with their damped sinusoid-like basis functions. Bessel expansion is used to emphasize the vowel regions by appropriate consideration of the range of Bessel coefficients. Bandpass filtered narrow-band signal is modeled as a monocomponent amplitude modulated-frequency modulated (AM-FM) signal. The amplitude envelope (AE) function of this vowel emphasized AM-FM signal gives strong evidence for the VOP and VEP. This evidence after adding with some of the existing evidences having source and system information, increases the detection rate as well as the accuracy of detection. © 2014 IEEE.
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    Offline signature verification based on contourlet transform and textural features using HMM
    (Institute of Electrical and Electronics Engineers Inc., 2014) Pushpalatha, K.N.; Supreeth Prajwal, S.S.; Gautam, A.K.; Shiva Kumar, K.B.S.
    Automatic offline signature verification and recognition is becoming essential in personal authentication. In this paper, we propose a transform domain offline signature verification system based on contourlet transform, directional features and Hidden Markov Model (HMM) as classifier. The signature image is preprocessed for noise removal and a two level contourlet transform is applied to get feature vector. The textural features are computed and concatenated with coefficients of contourlet transform to form the final feature vector. HTK tool with HMM classifier is used for classification. The parameters of False Rejection Rate (FRR), False Acceptance Rate (FAR) and Total Success Rate (TSR) are calculated for GPDS-960 database. It is found that the parameters of FRR and FAR are improved compared to the existing algorithms. © 2014 IEEE.