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.
Highlights
INTRODUCTIONAutomatic speech recognition is a key part in applications, for instance, speech record recovery and human-PC association
We found that Keyword Spotting system (KWS) operates only in a one language environment
Multilingual KWS and linguistic recognition [6][7] are vital to making spoken exchange frameworks which can work in a multilingual context
Summary
Automatic speech recognition is a key part in applications, for instance, speech record recovery and human-PC association. KWS is a procedure which is utilized to interpret precise words from a relentless talk [4]. It is fabulously of language ASR framework which is displayed to out of language words. Multilingual KWS and linguistic recognition [6][7] are vital to making spoken exchange frameworks which can work in a multilingual context. The NE India linguistic situation is different from the rest of India. This is the area where two large Indo-European and Tibeto-Burman. The structure of the paper is given below: Section II explains Keyword spotting, Section III clarifies the methodology approach, Section IV shows the result and Section V gives the conclusion
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