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What is Enhancing ASLID with PCA in Noise about?
This research presents an Automatic Spoken Language Identification (ASLID) framework that utilizes Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to improve language identification accuracy in noisy environments. The proposed system effectively reduces feature dimensionality while preserving significant variance, achieving an accuracy of up to 99.92% on the IIIT-H Indic speech dataset. The findings highlight the effectiveness of combining PCA with LDA for enhancing the robustness
- Author
- Diaa AbdelMoneim
- Language
- EN