{
  "annotation_id": "e36fc9e8-aaac-4738-9737-f5474c8617bc",
  "date_created": "2026-03-02T18:01:00.069000Z",
  "date_modified": "2026-03-02T18:01:00.069000Z",
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  "private": false,
  "record": {
    "abstract": "The success of nonlinear noise reduction applied to a single channel\nrecording of human voice is measured in terms of the recognition rate of a\ncommercial speech recognition program in comparison to the optimal linear\nfilter. The overall performance of the nonlinear method is shown to be\nsuperior. We hence demonstrate that an algorithm which has its roots in the\ntheory of nonlinear deterministic dynamics possesses a large potential in a\nrealistic application.",
    "arxiv_id": "physics/0507044",
    "authors": [
      "Krzysztof Urbanowicz",
      "Holger Kantz"
    ],
    "categories": [
      "physics.data-an",
      "physics.comp-ph"
    ],
    "doi": "10.1063/1.2743614",
    "journal_ref": "Chaos 17, 023121 (2007) (6 pages)",
    "title": "Improvement of speech recognition by nonlinear noise reduction",
    "url": "https://arxiv.org/abs/physics/0507044"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "6ecf3dd4-4f93-4d88-93cf-82aab69e1f0e",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}