SakhrML/SpeakMK1_SLP_Dialogue

该数据集包含1,000个多轮模拟儿科言语语言病理学（SLP）互动对话，专门设计用于训练、评估或微调大型语言模型，以使其能够扮演临床言语治疗师角色，或用于研究言语治疗过程中的临床推理。每个对话包括临床元数据（儿童年龄、言语声音障碍类别、特定音素错误、临床目标、复杂度水平和治疗策略）、显式顺序推理路径（model_thought）以及目标治疗响应（slp_response）。数据集总对话数为1,000个，总对话轮数为3,625轮，平均每个对话3.62轮，目标人群为3至10岁患有各种言语声音障碍的儿童。This dataset consists of 1,000 multi-turn simulated pediatric Speech-Language Pathology (SLP) interaction dialogues. It is specifically designed to train, evaluate, or fine-tune LLMs to act as clinical speech-language therapists or to study clinical reasoning during speech therapy sessions. Each conversation includes clinical metadata (child age, speech sound disorder category, specific phone error, clinical goal, complexity level, and therapeutic strategy), explicit sequential reasoning paths (`model_thought`), and the target therapeutic response (`slp_response`). - **Total Dialogues:** 1,000 - **Total Conversational Turns:** 3,625 - **Average Turns per Dialogue:** 3.62 - **Target Population:** Children aged 3–10 presenting with various speech sound disorders.