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arXiv · 2412.10857

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

Ali Nasr-Esfahani·Mehdi Bekrani·Roozbeh Rajabi
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Abstract

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 FARSDIGIT1 dataset, augmented with various approaches, is processed using Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction. Experimental results demonstrate the model's effectiveness, achieving 98.53%, 96.10%, and 95.92% accuracy on training, validation, and test sets, respectively. In noisy conditions, the proposed approach improves recognition by 26.88% 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 7.61%.

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Ali Nasr-Esfahani, Mehdi Bekrani, Roozbeh Rajabi. 2024-12-14. Robust Persian Digit Recognition in Noisy Environments Using Hybrid CNN-BiGRU Model. https://arxiv.org/abs/2412.10857

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