A Comparative Analysis of Recurrent Neural Networks-LSTM and 1D Convolutional Neural Network in Wake Word Detection System of Regional Dialects

Authors

  • Chaitra G. P., Shylaja S. S.

Keywords:

MFCC, CONV 1D, Dialect Identification, RNN(LSTM), Trainable Parameters, WWD.

Abstract

This paper aims in comparative analysis of two deep neural network techniques -RNNs and 1D-CNN models  in building the WWD system in Kannada for five various locations with their dialects in the state of Karnataka and they are:- Dharwad, Tulu, Dogg anal, Urban Kannada in addition the Kodagu region as well. The customized WWD system is built using Conv1d model with 97% accuracy compared to RNNs with 42.5% precision. The variation present with local dialects are finely specified by 1d CNN in analogous study along with RNN model in verifying on the dialect dataset on the labels impending which are contrasting and the implementation of Conv1d makes better predictions on the Idiom Dataset.

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Published

26.03.2024

How to Cite

Chaitra G. P. (2024). A Comparative Analysis of Recurrent Neural Networks-LSTM and 1D Convolutional Neural Network in Wake Word Detection System of Regional Dialects. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3742 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6124

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Section

Research Article