Isolated Keyword Spotting in Multilingual Environment using ANN and MFCC 
Brajen Kumar Deka1, Pranab Das2
1Brajen Kumar Deka*, Research Scholar, Department of Computer Applications, Assam Don Bosco University, Guwahati, India.
2Pranab Das, Assistant Professor (Sr.), Department of Computer Applications, Assam Don Bosco University, Guwahati, India.
Manuscript received on April 05, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 5-8 | Volume-9 Issue-4, April 2020. | Retrieval Number: C6135029320/2020©BEIESP | DOI: 10.35940/ijeat.C6135.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The performance and analysis of Keyword Spotting system (KWS) are applied when the training and testing in a multilingual environment. This paper exhibits an approach for building up a multilingual KWS framework for Assamese, English and Hindi language dependent on feed-forward neural system. Mel Frequency Cepstral Coefficient (MFCC) has been utilized for highlight extraction which gives a lot of highlight vectors from recorded sound examples. Neural Network backpropagation model is utilized to improve the acknowledgment execution on the recently made multilingual database utilizing the multi-layer feed-forward neural system classifier.
Keywords: ANN, Backpropagation, Keyword Spotting, MFCC.