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

• URL: http://arxiv.org/abs/2412.10857v2
• Date: Tue, 11 Feb 2025 07:01:26 GMT
• Title: Robust Persian Digit Recognition in Noisy Environments Using Hybrid CNN-BiGRU Model
• Authors: Ali Nasr-Esfahani, Mehdi Bekrani, Roozbeh Rajabi,
• Abstract summary: This study addresses isolated spoken Persian digit recognition (zero to nine) under noisy conditions.<n>A hybrid model combining residual convolutional neural networks and bidirectional gated units (BiGRU) is proposed.<n> Experimental results demonstrate the model's effectiveness, achieving 98.53%, 96.10%, and 95.92% accuracy on training, validation, and test sets.
• Score: 1.5566524830295307
• License: http://creativecommons.org/licenses/by/4.0/
• 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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