"senone" meaning in English

{
  "forms": [
    {
      "form": "senones",
      "tags": [
        "plural"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {},
      "expansion": "senone (plural senones)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "categories": [
        {
          "kind": "other",
          "name": "English entries with incorrect language header",
          "parents": [
            "Entries with incorrect language header",
            "Entry maintenance"
          ],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "Pages with 1 entry",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "other",
          "name": "Pages with entries",
          "parents": [],
          "source": "w"
        },
        {
          "kind": "topical",
          "langcode": "en",
          "name": "Linguistics",
          "orig": "en:Linguistics",
          "parents": [
            "Language",
            "Social sciences",
            "Communication",
            "Sciences",
            "Society",
            "All topics",
            "Fundamental"
          ],
          "source": "w"
        }
      ],
      "examples": [
        {
          "ref": "2016, Seyed Omid Sadjadi, Sriram Ganapathy, Jason W. Pelecanos, “The IBM 2016 Speaker Recognition System”, in arXiv:",
          "text": "Experimental results indicate that: 1) the NDA is more effective (up to 35% relative improvement in terms of EER) than the traditional parametric LDA for speaker recognition, 2) when compared to raw acoustic features (e.g., MFCCs), the ASR speaker-adapted features provide gains in speaker recognition performance, and 3) increasing the number of output units in the DNN acoustic model (i.e., increasing the senone set size from 2k to 10k) provides consistent improvements in performance (for example from 37% to 57% relative EER gains over our baseline GMM i-vector system).",
          "type": "quote"
        }
      ],
      "glosses": [
        "A subset of a phone (speech segment)"
      ],
      "id": "en-senone-en-noun-XvdWOnh8",
      "links": [
        [
          "linguistics",
          "linguistics"
        ],
        [
          "subset",
          "subset"
        ],
        [
          "phone",
          "phone"
        ]
      ],
      "raw_glosses": [
        "(linguistics) A subset of a phone (speech segment)"
      ],
      "topics": [
        "human-sciences",
        "linguistics",
        "sciences"
      ]
    }
  ],
  "word": "senone"
}

{
  "forms": [
    {
      "form": "senones",
      "tags": [
        "plural"
      ]
    }
  ],
  "head_templates": [
    {
      "args": {},
      "expansion": "senone (plural senones)",
      "name": "en-noun"
    }
  ],
  "lang": "English",
  "lang_code": "en",
  "pos": "noun",
  "senses": [
    {
      "categories": [
        "English countable nouns",
        "English entries with incorrect language header",
        "English lemmas",
        "English nouns",
        "English terms with quotations",
        "Pages with 1 entry",
        "Pages with entries",
        "en:Linguistics"
      ],
      "examples": [
        {
          "ref": "2016, Seyed Omid Sadjadi, Sriram Ganapathy, Jason W. Pelecanos, “The IBM 2016 Speaker Recognition System”, in arXiv:",
          "text": "Experimental results indicate that: 1) the NDA is more effective (up to 35% relative improvement in terms of EER) than the traditional parametric LDA for speaker recognition, 2) when compared to raw acoustic features (e.g., MFCCs), the ASR speaker-adapted features provide gains in speaker recognition performance, and 3) increasing the number of output units in the DNN acoustic model (i.e., increasing the senone set size from 2k to 10k) provides consistent improvements in performance (for example from 37% to 57% relative EER gains over our baseline GMM i-vector system).",
          "type": "quote"
        }
      ],
      "glosses": [
        "A subset of a phone (speech segment)"
      ],
      "links": [
        [
          "linguistics",
          "linguistics"
        ],
        [
          "subset",
          "subset"
        ],
        [
          "phone",
          "phone"
        ]
      ],
      "raw_glosses": [
        "(linguistics) A subset of a phone (speech segment)"
      ],
      "topics": [
        "human-sciences",
        "linguistics",
        "sciences"
      ]
    }
  ],
  "word": "senone"
}