{
  "id": "1204.4257",
  "version": "v1",
  "published": "2012-04-19T06:10:02.000Z",
  "updated": "2012-04-19T06:10:02.000Z",
  "title": "Speech Recognition: Increasing Efficiency of Support Vector Machines",
  "authors": [
    "Aamir Khan",
    "Muhammad Farhan",
    "Asar Ali"
  ],
  "comment": "5 pages, 11 figures. arXiv admin note: text overlap with arXiv:1201.3720 and arXiv:1204.1177",
  "journal": "International Journal of Computer Applications 35(7):17-21, December 2011",
  "categories": [
    "cs.CV"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2012-04-19T06:10:02.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "support vector machines",
      "speech recognition",
      "increasing efficiency",
      "error-free biometric identity verification systems",
      "biometric system"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2012arXiv1204.4257K"
    }
  }
}