IDEMITSU/hoiku-yougo-stt-ja

{
  "term": "用語（表記）",
  "reading": "よみがな（ひらがな）",
  "category": "カテゴリ",
  "priority": "優先度（最高／高／中／低）",
  "tags": ["タグ1", "タグ2"],
  "description": "用語の説明文",
  "mis_conversions": [
    "STTが誤認識しやすいパターン1（同音異義語）",
    "STTが誤認識しやすいパターン2（近似音）",
    "STTが誤認識しやすいパターン3（語尾変化）"
  ],
  "stt_oral_patterns": [
    "保護者・保育士が実際に発話するパターン1",
    "保護者・保育士が実際に発話するパターン2"
  ],
  "contexts": [
    "実際の保育現場での使用文例1",
    "実際の保育現場での使用文例2"
  ],
  "related_terms": ["関連用語1", "関連用語2"]
}

import json

with open("hoiku_yougo_v6.json", encoding="utf-8") as f:
    dataset = json.load(f)

# 最高優先度の用語を抽出してカスタム辞書を構築
high_priority_terms = [
    {
        "term": t["term"],
        "reading": t["reading"],
        "mis_conversions": t["mis_conversions"]
    }
    for t in dataset["terms"]
    if t["priority"] == "最高"
]

print(f"最高優先度用語数: {len(high_priority_terms)}")
# → 53件

# 安全管理カテゴリの用語でRAG用ベクトルDBを構築
safety_terms = [
    t for t in dataset["terms"]
    if t["category"] == "安全管理"
]

for term in safety_terms:
    # contextsをRAGの検索対象テキストとして使用
    for context in term["contexts"]:
        vector_db.add(
            text=context,
            metadata={"term": term["term"], "priority": term["priority"]}
        )

# 保護者の発話パターンを使って意図分類
oral_patterns = {}
for term in dataset["terms"]:
    for pattern in term.get("stt_oral_patterns", []):
        oral_patterns[pattern] = {
            "term": term["term"],
            "category": term["category"],
            "priority": term["priority"]
        }

# 例：「今日お休みします」→ 欠席連絡カテゴリに分類

{
  "term": "誤飲",
  "reading": "ごいん",
  "category": "安全管理",
  "priority": "最高",
  "tags": ["事故防止", "乳幼児", "異物"],
  "description": "様々な物への興味から口に入れ誤って飲み込むこと。乳幼児は特に注意が必要で、重大事故につながる場合がある。",
  "mis_conversions": ["語韻", "誤音", "五飲"],
  "stt_oral_patterns": [
    "誤飲しました",
    "飲み込んでしまって",
    "何か口に入れて",
    "異物を飲んで"
  ],
  "contexts": [
    "「誤飲を防ぐため、小さな部品は乳幼児の手の届かない場所に保管してください」",
    "「誤飲事故が発生した場合は直ちに救急対応を取り、飲み込んだものを確認します」",
    "「誤飲はヒヤリ・ハット報告で最も多いカテゴリのひとつです」"
  ],
  "related_terms": ["ヒヤリ・ハット", "窒息", "アナフィラキシー"]
}

@dataset{infomart_hoiku_stt_2026,
  title     = {保育用語STT最適化データセット / Japanese Childcare Vocabulary Dataset for STT},
  author    = {株式会社インフォマート / Infomart Co., Ltd.},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/IDEMITSU/hoiku-yougo-stt-ja},
  note      = {ヒヤリん（Hiyarin）開発知見より構築}
}

{
  "term": "Term (written form)",
  "reading": "Phonetic reading (hiragana)",
  "category": "Category",
  "priority": "Priority level (最高/高/中/低)",
  "tags": ["tag1", "tag2"],
  "description": "Definition and contextual explanation",
  "mis_conversions": [
    "Common STT misrecognition pattern 1 (homophone)",
    "Common STT misrecognition pattern 2 (near-phonetic)",
    "Common STT misrecognition pattern 3 (truncation/variation)"
  ],
  "stt_oral_patterns": [
    "Actual spoken pattern by parents or nursery staff 1",
    "Actual spoken pattern by parents or nursery staff 2"
  ],
  "contexts": [
    "Real-world usage example in childcare settings 1",
    "Real-world usage example in childcare settings 2"
  ],
  "related_terms": ["related term 1", "related term 2"]
}

import json

with open("hoiku_yougo_v6.json", encoding="utf-8") as f:
    dataset = json.load(f)

# Extract critical-priority terms for custom dictionary
critical_terms = [
    {
        "term": t["term"],
        "reading": t["reading"],
        "mis_conversions": t["mis_conversions"]
    }
    for t in dataset["terms"]
    if t["priority"] == "最高"
]
# → 53 terms covering life-safety vocabulary

# Build vector DB from safety management terms
safety_terms = [
    t for t in dataset["terms"]
    if t["category"] == "安全管理"
]

for term in safety_terms:
    for context in term["contexts"]:
        vector_db.add(
            text=context,
            metadata={
                "term": term["term"],
                "priority": term["priority"],
                "category": term["category"]
            }
        )

# Map spoken patterns to intent categories
intent_map = {}
for term in dataset["terms"]:
    for pattern in term.get("stt_oral_patterns", []):
        intent_map[pattern] = {
            "term": term["term"],
            "category": term["category"],
            "priority": term["priority"]
        }

# Example: "今日お休みします" ("Taking today off")
# → maps to 欠席連絡 (Absence Notification) category

{
  "term": "不適切保育",
  "reading": "ふてきせつほいく",
  "category": "不適切保育・権利擁護",
  "priority": "最高",
  "tags": ["行政", "権利侵害", "こども家庭庁", "通報", "保育の質"],
  "description": "子どもの人権・尊厳を傷つける保育行為の総称。体罰・暴言・脅し・無視・強制的な排泄・食事の強要など。こども家庭庁が2023年に実態調査を実施し、全国の保育施設に自己点検を義務づけた。",
  "mis_conversions": ["不適切補育", "不的切保育", "ふてきせつ保育"],
  "stt_oral_patterns": [
    "不適切保育があった",
    "不適切な対応をしてしまった",
    "不適切保育の疑いがある",
    "不適切保育として報告する",
    "これは不適切保育にあたりますか"
  ],
  "contexts": [
    "「不適切保育の疑いがある場合は、一人で抱え込まず管理者に報告してください」",
    "「こども家庭庁の指針では、不適切保育を発見した職員には報告義務があります」",
    "「行政の実地指導では不適切保育の有無が重点確認事項になっています」"
  ],
  "related_terms": ["体罰", "暴言", "ネグレクト", "権利擁護", "自己点検", "内部通報"]
}

@dataset{infomart_hoiku_stt_2026,
  title     = {Japanese Childcare Vocabulary Dataset for STT Optimization (保育用語STT最適化データセット)},
  author    = {Infomart Co., Ltd. (株式会社インフォマート)},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/IDEMITSU/hoiku-yougo-stt-ja},
  note      = {Built from operational knowledge of Hiyarin, an AI-powered childcare safety reporting system}
}