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Spoken Keyword Spotting System Design Using Various Wavelet Transformation Techniques with BPNN Classifier

Senthil Devi K. A., Dr. B. Srinivasan ,

Affiliations
Assistant Professor, Gobi Arts & Science College, Tamil Nadu, India. 2 Associate Professor, Gobi Arts & Science College, Tamil Nadu, India.
:10.22362/ijcert/2017/v4/i3/xxxx [UNDER PROCESS]


Abstract
SpokenKeyword spotting is a speech data mining task which is used to search audio signals for finding occurrences of a specified spoken word in the given speech file.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, keyword spotting system designed with various wavelet transformation techniques and BackpropagationNeural Network (BPNN). Back Propagation Neural Network (BPNN) is trained with two 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 tested with ten speech content often different speakers. Various statistical features extraction techniques with wavelet transformation are used. Performance comparison is done among these methods with Haar, Daubechies2 and Simlet 4 wavelets.


Citation
Senthil Devi K. A et.al, “Spoken Keyword Spotting System Design Using Various Wavelet Transformation Techniques with BPNN Classifier”, International Journal Of Computer Engineering In Research Trends, 4(3):111-118, March-2017.


Keywords : Spoken keyword spotting, Speech data mining, MFCC, Wavelet Packet Decomposition, Discrete Wavelet Transformation, BPN neural network, wavelet families.

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