Browsing by Author "Prasanna, S.R.M."

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    Development of online isolated digit recognizer using hidden Markov model
    (2013) Supreeth, Prajwal, S.; Raju, V.K.; Lalitesh, M.; Banriskhem, K.K.; Prasanna, S.R.M.
    The objective of this work is to develop an online isolated digit recognizer that takes small footprint and also runs faster than that using the conventional Hidden Markov Model (HTK) toolkit. Such system will find applications on devices with limited computing power and memory capacity. The different modules in the online digit recognizer are explained first. This is followed by the development of offline digit recognizer in MATLAB. Later the online digit recognizer is implemented in C language. The performance evaluation of the digit recognizer is made in both offline and online mode. The digit recognizer works well except for digit two and eight where the confusion is high. However, the online system works well in speaker independent scenario.
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    Development of online isolated digit recognizer using hidden Markov model
    (Institution of Engineering and Technology journals@theiet.org, 2013) Supreeth Prajwal, S.; Raju, V.K.; Lalitesh, M.; Khonglah, K.K.; Prasanna, S.R.M.
    The objective of this work is to develop an online isolated digit recognizer that takes small footprint and also runs faster than that using the conventional Hidden Markov Model (HTK) toolkit. Such system will find applications on devices with limited computing power and memory capacity. The different modules in the online digit recognizer are explained first. This is followed by the development of offline digit recognizer in MATLAB. Later the online digit recognizer is implemented in C language. The performance evaluation of the digit recognizer is made in both offline and online mode. The digit recognizer works well except for digit two and eight where the confusion is high. However, the online system works well in speaker independent scenario.
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    Improved Vowel Onset and offset points detection using bessel features
    (2014) Sarma, B.D.; 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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    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.