Robust Recognition of Persian Isolated Digits in Speech using Deep Neural Network

In recent years, artificial intelligence (AI) has advanced significantly in speech recognition applications. Speech-based interaction with digital systems, particularly AI-driven digit recognition, has emerged as a prominent application. However, existing neural network-based methods often neglect the impact of noise, leading to reduced accuracy in noisy environments. This study tackles the challenge of recognizing the isolated spoken Persian numbers (zero to nine), particularly distinguishing phonetically similar numbers, in noisy environments. The proposed method, which is designed for speaker-independent recognition, combines residual convolutional neural network and bidirectional gated recurrent unit in a hybrid structure for Persian number recognition. This method employs word units as input instead of phoneme units. Audio data from 51 speakers of FARSDIGIT1 database are utilized after augmentation using various noises, and the Mel-Frequency Cepstral Coefficients (MFCC) technique is employed for feature extraction. The experimental results show the proposed method efficacy with 98.53%, 96.10%, and 95.9% recognition accuracy for training, validation, and test, respectively. In the noisy environment, the proposed method exhibits an average performance improvement of 26.88% over phoneme unit-based LSTM method for Persian numbers. In addition, the accuracy of the proposed method is 7.61% better than that of the Mel-scale Two Dimension Root Cepstrum Coefficients (MTDRCC) feature extraction technique along with MLP model in the test data for the same dataset.