Deep Learning Approach for Spoken Digit Recognition in Gujarati Language

Speech Recognition is an emerging field in the area of Natural Language Processing which provides ease for human machine interaction with speech. Speech recognition for digits is useful for numbers oriented communication such as mobile number, scores, account number, registration code, social security code etc. This research paper seeks to achieve recognition of ten Gujarati digits from zero to nine (૦ to ૯) by using a deep learning approach. Dataset is generated with total 8 native speakers 4 male 4 female with the age group of 20 to 40. The dataset includes 2400 labeled audio clips of both genders. To implement a deep learning approach, Convolutional Neural Network (CNN) with MFCC is used to analyze audio clips to generate spectrograms. For the proposed approach three different experiments were performed with different dataset sizes as 1200, 1800 and 2400. With this approach maximum 98.7% accuracy is achieved for spoken digits in Gujarati language with 98% Precision and 98% Recall. It is analyzed from various experiments that increase in dataset size improves the accuracy rate for spoken digit recognition. No of epochs in CNN also improves accuracy to some extent.

@article{Tailor2022,
  title     = {Deep Learning Approach for Spoken Digit Recognition in Gujarati Language},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {4},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Jinal H. Tailor and Rajnish Rakholia and Jatinderkumar R. Saini and Ketan Kotecha},
  doi       = {10.14569/IJACSA.2022.0130450},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130450}
}