ElBedwehy, M., Mayyalou, K., Behery, G., elbarougy, R. (2025). Improving Spoken Language Identification in Noisy Environment Based on Feature Reduction Using PCA. Journal of Computing and Communication, 4(2), 45-62. doi: 10.21608/jocc.2025.446638
Mona Nagy ElBedwehy; Kholoud Mayyalou; G. M. Behery; Reda elbarougy. "Improving Spoken Language Identification in Noisy Environment Based on Feature Reduction Using PCA". Journal of Computing and Communication, 4, 2, 2025, 45-62. doi: 10.21608/jocc.2025.446638
ElBedwehy, M., Mayyalou, K., Behery, G., elbarougy, R. (2025). 'Improving Spoken Language Identification in Noisy Environment Based on Feature Reduction Using PCA', Journal of Computing and Communication, 4(2), pp. 45-62. doi: 10.21608/jocc.2025.446638
ElBedwehy, M., Mayyalou, K., Behery, G., elbarougy, R. Improving Spoken Language Identification in Noisy Environment Based on Feature Reduction Using PCA. Journal of Computing and Communication, 2025; 4(2): 45-62. doi: 10.21608/jocc.2025.446638
Improving Spoken Language Identification in Noisy Environment Based on Feature Reduction Using PCA
1Department of Computer Science, Faculty of Computer and Artificial Intelligence, Damietta University, Egypt
2Department of Information Technology, Faculty of Computer and Artificial Intelligence, Damietta University, Egypt
Abstract
Automatic Spoken Language Identification (ASLID) is essential for effective multilingual communication, especially in real-world environments characterized by noise and acoustic variability where noise significantly impacts performance. This research introduces a robust ASLID framework that highlights the significance of feature reduction via principal components analysis (PCA) integrated with linear discriminant analysis (LDA) to enhance classification performance in noisy environments. The system utilizes OpenSMILE to extract extensive audio features, capturing diverse speech characteristics necessary for accurate language discrimination. To address the high dimensionality and redundancy inherent in the feature set, PCA is employed to reduce the feature space, preserving the most significant variance and enhancing computational efficiency. Following PCA, LDA is applied to maximize class separability, further refining the feature space for effective language classification. The proposed approach is evaluated on a benchmark dataset under various noise levels and test set proportions. Extensive experiments conducted on the IIIT-H Indic speech dataset demonstrate that the proposed PCA-LDA approach outperforms traditional methods, achieving an accuracy of up to 99.92% in noisy conditions, even with reduced feature dimensions. Experimental results demonstrate that integrating PCA with LDA significantly improves accuracy and robustness, outperforming conventional feature selection and classification techniques. The findings affirm that the combined PCA-LDA strategy effectively enhances the resilience of ASLID systems in challenging acoustic environments, making it a promising solution for practical multilingual speech processing applications.
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