Speech Recognition: Increasing Efficiency of Support Vector Machines
This work addresses the need for robust and error-free biometric systems in communication and security, but it appears incremental as it builds on existing methods like SVMs and LDA.
The paper tackled the problem of improving biometric identity verification systems by combining Support Vector Machines (SVMs) and Linear Discriminant Analysis (LDA) with MFCCs for speech recognition, resulting in a multimodal system that shows good pattern performance.
With the advancement of communication and security technologies, it has become crucial to have robustness of embedded biometric systems. This paper presents the realization of such technologies which demands reliable and error-free biometric identity verification systems. High dimensional patterns are not permitted due to eigen-decomposition in high dimensional feature space and degeneration of scattering matrices in small size sample. Generalization, dimensionality reduction and maximizing the margins are controlled by minimizing weight vectors. Results show good pattern by multimodal biometric system proposed in this paper. This paper is aimed at investigating a biometric identity system using Support Vector Machines(SVMs) and Lindear Discriminant Analysis(LDA) with MFCCs and implementing such system in real-time using SignalWAVE.