Voice Intelligence Based Wake Word Detection of Regional Dialects using 1D Convolutional Neural Network

Abstract

Abstract— Voice-based apps can be effective among rural farmers, if it is in their own spoken language/dialect. Many voice-based apps were developed in the agricultural sector, and in each case, farmers had to either type in the queries or they had to communicate with the device which had the standard speech to deliver the solution which added to the challenge to comprehend the information. This paper presents the research work in developing the wake word detection system for major dialects based on 5 different regions in Karnataka, namely-Dharwad, Dogganal, Tulu, Kodagu and Urban Kannada.The customized wake word system is designed using 1D CNN model with 98% accuracy which showed better results over ANNs with 14.1% and RNNs with 48.1% accuracies. The diversity in regional dialects has been well identified using Conv1D model and with comparative analysis with RNNs to validate on the sequential data the predicted labels were compared and the performance of Conv1d reconciles well for the Dialect Dataset.