A Novel User Interface for Text Dependent Human Voice Recognition System
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2018-09-25 https://doi.org/10.14419/ijet.v7i4.6.20714
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Additive Prognostication (AP), band-pass filtering, feature extraction, human voice, recognition rate, Wavelet decomposition/reconstruction tree. -
Abstract
In an effort to provide a more efficient representation of the speech signal, the application of the wavelet analysis is considered. This research presents an effective and robust method for extracting features for speech processing. Here, we proposed a novel user interface for Text Dependent Human Voice Recognition (TD-HVR) system. The proposed HVR model utilizes decimated bi-orthogonal wavelet transform (DBT) approach to extract the low level features from the given input voice signal, then the noise elimination will be done by band pass filtering followed by normalization for better quality of a voice signal and finally the formants of a train and test voices will be calculated by using the Additive Prognostication (AP) algorithm. Simulation results have been compared with the existing HVR schemes, and shown that the proposed user interface system has performed superior to the conventional HVR systems with an accuracy rate of approximately 99 %.
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References
Soontorn Oraintara, Ying-Jui Chen Et.al. IEEE Transactions on Signal Processing, IFFT, Vol. 50, No. 3, March 2002.
[2] Kelly Wong, Journal of Undergraduate Research, The Role of the Fourier Transform in Time-Scale Modification, University of Florida, Vol 2, Issue 11 - August 2011.
[3] Bao Liu, Sherman Riemenschneider, An Adaptive Time Frequency Representation and Its Fast Implementation, Department of Mathematics, West Virginia University.
[4] Viswanath Ganapathy, Ranjeet K. Patro, Chandrasekhara Thejaswi, ManikRaina, Subhas K.Ghosh, Signal Separation using Time Frequency Representation, Honeywell Technology Solutions Laboratory.
[5] Amara Graps, An Introduction to Wavelets, Istituto di Fisica dello Spazio Interplanetario, CNR-ARTOV BraniVidakovic and Peter Mueller, Wavelets For Kids– A Tutorial Introduction, Duke University.
[6] O. Farooq and S. Datta, A Novel Wavelet Based Pre Processing For Robust Features In ASR.
[7] GiulianoAntoniol, Vincenzo Fabio Rollo, Gabriele Venturi, IEEE Transactions on Software Engineering, LPC & Cepstrum coefficients for Mining Time Variant Information from Software Repositories, University of Sannio, Italy.
[8] Michael Unser, Thierry Blu, IEEE Transactions on Signal Processing, Wavelet Theory Demystified, Vol.51,No. 2,Feb’13.
[9] C. Valens, IEEE, A Really Friendly Guide to Wavelets,Vol.86, No. 11, Nov 2012.
[10] James M. Lewis, C. S Burrus, Approximate CWT with An Application To Noise Reduction, Rice University, Houston.
[11] Ted Painter, Andreas Spanias, IEEE, Perceptual Coding of Digital Audio, ASU.
[12] D P. W. Ellis, PLP,RASTA, MFCC & inversion Matlab, 2005
[13] Ram Singh, Proceedings of the NCC, Spectral Subtraction Speech Enhancement with RASTA Filtering IIT-B 2012.
[14] NitinSawhney, Situational Awareness from Environmental Sounds, SIG, MIT Media Lab, June 13, 2013.
[15] Rami Al-Hmouz, Khaled and Ali, “Multimodal Biometrics Using Multiple Feature Representations toSpeaker Identification Systemâ€, InternationalConference on Information and Communication Technology Research (ICICTR), 2015.
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How to Cite
P, R., & ., . (2018). A Novel User Interface for Text Dependent Human Voice Recognition System. International Journal of Engineering & Technology, 7(4.6), 285-290. https://doi.org/10.14419/ijet.v7i4.6.20714Received date: 2018-10-01
Accepted date: 2018-10-01
Published date: 2018-09-25