Poulose Jacob,K; Sonia, Sunny; David, Peter S(IEEE, August 9, 2012)
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Abstract:
Speech signals are one of the most important means
of communication among the human beings. In this paper, a
comparative study of two feature extraction techniques are
carried out for recognizing speaker independent spoken
isolated words. First one is a hybrid approach with Linear
Predictive Coding (LPC) and Artificial Neural Networks
(ANN) and the second method uses a combination of Wavelet
Packet Decomposition (WPD) and Artificial Neural Networks.
Voice signals are sampled directly from the microphone and
then they are processed using these two techniques for
extracting the features. Words from Malayalam, one of the
four major Dravidian languages of southern India are chosen
for recognition. Training, testing and pattern recognition are
performed using Artificial Neural Networks. Back propagation
method is used to train the ANN. The proposed method is
implemented for 50 speakers uttering 20 isolated words each.
Both the methods produce good recognition accuracy. But
Wavelet Packet Decomposition is found to be more suitable for
recognizing speech because of its multi-resolution
characteristics and efficient time frequency localizations
Description:
Advances in Computing and Communications (ICACC), 2012 International Conference on
Devassia, V P; Dr. Tessamma, Thomas(Cochin University of Science & Technology, December , 2003)
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Abstract:
During 1990's the Wavelet Transform emerged as an important signal processing tool with potential applications in time-frequency analysis and non-stationary signal processing.Wavelets have gained popularity in broad range of disciplines like signal/image compression, medical diagnostics, boundary value problems, geophysical signal processing, statistical signal processing,pattern recognition,underwater acoustics etc.In 1993, G. Evangelista introduced the Pitch- synchronous Wavelet Transform, which is particularly suited for pseudo-periodic signal processing.The work presented in this thesis mainly concentrates on two interrelated topics in signal processing,viz. the Wavelet Transform based signal compression and the computation of Discrete Wavelet Transform. A new compression scheme is described in which the Pitch-Synchronous Wavelet Transform technique is combined with the popular linear Predictive Coding method for pseudo-periodic signal processing. Subsequently,A novel Parallel Multiple Subsequence structure is presented for the efficient computation of Wavelet Transform. Case studies also presented to highlight the potential applications.
Description:
Department of Electronics, Cochin University of Science and Technology