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]
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.
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.
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