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Paper Details
Paper Title
Wavelet – Neural Data Mining Approach for Spoken Keyword Spotting
Authors
  K. A. Senthil Devi,  DR. B. Srinivasan
Abstract
Spoken keyword spotting is a technologically relevant problem in speech
data mining. It is essential to identify the occurrences of specified keywords expertly
from lots of hours of speech contents such as meetings, lectures, etc. In this paper,
Wavelet Packet Decomposition (WPD) and Neural Network (NN) based data mining
model (WPDNNM) is explored for keyword spotting. Speech data is first
decomposed with Haar, Daubechies2 and Simlet4 wavelets packets. Then, some
significant features are extracted from the decomposed speech data. Back
Propagation Neural Network (BPNN) is trained with three predefined spoken
keywords based on known features and finally, input speech features are compared
with keyword features in the trained BPNN for spotting the occurrences of the
specified keyword. The method of this paper is tested with 5 minutes lecture data.
This method is compared with Discrete Wavelet Transformation (DWT) feature
extraction based keyword spotting. Experimental results show that the wavelet -
neural method with WPD of Daubechies2 wavelet is more accurate than with Haar
and Simlet4 wavelets.
Keywords- Spoken keyword spotting, Speech data mining, Wavelet Packet Decomposition, Discrete Wavelet Transformation, BPN neural network, word detection
Publication Details
Unique Identification Number - IJEDR1701086Page Number(s) - 569-576Pubished in - Volume 5 | Issue 1 | March 2017DOI (Digital Object Identifier) -    Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
  K. A. Senthil Devi,  DR. B. Srinivasan,   "Wavelet – Neural Data Mining Approach for Spoken Keyword Spotting", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 1, pp.569-576, March 2017, Available at :http://www.ijedr.org/papers/IJEDR1701086.pdf
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