Abstract: In human-computer interaction, humans can interact with the computer with the help of text, audio, images, speech, etc. Interacting with the computer using speech, speech recognitions in particularly audio segmentation is a challenging task due to accent or way of pronouncing style. To input mathematical symbols, words, functions, and expressions with the help of a keyboard are tedious and time-consuming. Input this with the help of audio, speeds up the input process. In this paper, an SBTI (audio Segmentation Based Token Identification) algorithm is proposed for the recognition of words in an audio mathematical expression. 6 types of audio mathematical expressions are considered for recognition. The proposed algorithm segments the audio file into chunks and from each chunk temporal and spectral characteristics of audio signals are selected to extract the features. The model is trained using a neural network. The proposed algorithm shows a classification accuracy of 100% for the algebraic, quadratic, area, and differentiation expression, 99% for trigonometric expression, and 92% for summation expression.

Vaishali A. Kherdekar, Sachin A. Naik and Prafulla Bafna, “A Segmentation-based Token Identification for Recognition of Audio Mathematical Expression” International Journal of Advanced Computer Science and Applications(IJACSA), 14(7), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140733

@article{Kherdekar2023,
title = {A Segmentation-based Token Identification for Recognition of Audio Mathematical Expression},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140733},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140733},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Vaishali A. Kherdekar and Sachin A. Naik and Prafulla Bafna}
}