Automated Recognition of Hindi Word Audio Clips for Indian Children using Clustering-based Filters and Binary Classifier

ABSTRACT

Speech recognition systems have made remarkable progress in the last few decades but most of the work has been done for adult speech. The rise of online learning during Covid-19 pandemic highlights the need for voice-enabled assistants for children so that they can navigate the menus and interfaces seamlessly. Speech recognition for children will also be very useful to develop automated reading assessment tools. However, such technology for children is challenging for a country like India where differences in accents, diction and enunciation is significant but available children speech data is limited. Through this paper, I tried various approaches to recognize hindi word audios. Commercially available Google Speech-to-Text performs poorly with only 49.7% accuracy at recall of 0.24 while recognising audio samples containing hindi words spoken by children. Using the same dataset, I experimented with clustering algorithm and logistic regression and found that the accuracy improves upto 81% with logistic regression. The paper also highlights the importance of data preprocessing by performing noise reduction using Butterworth low pass filters. © ICNLSP 2021. All Rights Reserved.