Robust Persian Digit Recognition in Noisy Environments Using Hybrid CNN-BiGRU Model

Artificial intelligence (AI) has significantly advanced speech recognition applications. However, many existing neural network-based methods struggle with noise, reducing accuracy in real-world environments. This study addresses isolated spoken Persian digit recognition (zero to nine) under noisy conditions, particularly for phonetically similar numbers. A hybrid model combining residual convolutional neural networks and bidirectional gated recurrent units (BiGRU) is proposed, utilizing word units instead of phoneme units for speaker-independent recognition. The FARSDIGIT۱ dataset, augmented with various approaches, is processed using Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction. Experimental results demonstrate the model’s effectiveness, achieving ۹۸.۵۳%, ۹۶.۱۰%, and ۹۵.۹۲% accuracy on training, validation, and test sets, respectively. In noisy conditions, the proposed approach improves recognition by ۲۶.۸۸% over phoneme unit-based LSTM models and surpasses the Mel-scale Two Dimension Root Cepstrum Coefficients (MTDRCC) feature extraction technique along with MLP model (MTDRCC+MLP) by ۷.۶۱%.

کلیدواژه ها:

Spoken Digit Recognition ، data augmentation ، Convolutional neural network ، Bidirectional Gated Recurrent Unit