--- title: "Human-Robot Conversation Dataset (Korean)" description: "Human-robot dataset is an audio dataset containing 660+ hours of Korean dialogues between an AI and humans across 20,000 recordings. This conversation dataset includes short…" url: "https://unidata.pro/datasets/human-robot-conversation-korean/" date_modified: "2026-04-09T13:20:51+03:00" language: "en-US" --- Human-robot dataset is an audio dataset containing 660+ hours of Korean dialogues between an AI and humans across 20,000 recordings. This conversation dataset includes short M4A and WAV audio files (up to 2 minutes) with structured metadata, supporting research in human-robot interactions, Korean natural language processing, and AI-driven dialogue systems. ## Dataset Structure ### The Numbers Section **Numbered list:** - **Number:** 660+ — **Text:** Hours - **Number:** 20,000 — **Text:** Files ### Tooltips Section **Tooltip items:** - **Name:** Voice Assistant - **Name:** ASR - **Name:** Machine Learning - **Name:** Audio Processing - **Name:** Voice Recognition ### Dataset Information **Table with data:** | Characteristic | Data | | --- | --- | | Description | Description Audio of Korean dialogues between AI and humans | | Data types | Audio | | Tasks | Speech Recognition, LLM | | Hours of audio | 660+ | | Number of sets | 20,000 | | Language | Korean | | Labeling | Metadata (id, language, format) | **Media Slider:** - **Video on Slayder:** **Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1LqRO0CZkMnPY-muq7rHp9eIStsZh2T1Q) ### Technical Specifications **Table with data:** | Characteristic | Data | | --- | --- | | Audio Format | M4A, WAV | | Duration | Max = 2 min | **Source and data collection methodology:** Source and collection methodology: Data was collected via crowdsourcing platforms. ### Dataset Use Cases - Slider **Industry Cards:** - **Industry:** Robotics & Human-Robot Interaction — **Title:** Training Conversational Systems for Social Robots — **Text:** This conversation dataset supports the development of conversational systems used in service robots, companion robots, and public-facing AI assistants. The dialogue dataset captures realistic AI-human interactions, helping language models learn how people speak to machines in everyday situations. Researchers can analyze conversation structure, emotional speech patterns, and response timing to improve natural communication between humans and social robots. - **Industry:** Artificial Intelligence Research — **Title:** Studying Human vs Robot Communication Behavior — **Text:** The AI human conversation dataset enables researchers to examine how people communicate with artificial intelligence systems. By analyzing human vs robot Korean dialogue audio, scientists can study language choices, conversational context, and interaction dynamics. The dataset provides reliable data for intelligence research, benchmarking conversational models, and improving how AI systems understand natural human conversation. - **Industry:** Customer Experience & Virtual Assistants — **Title:** Building More Natural AI-Human Support Agents — **Text:** Companies developing conversational assistants can use this Korean dialogue dataset to train AI systems for customer communication scenarios. The audio dataset contains real conversational speech that helps models learn natural sentences, context handling, and human interaction patterns. These insights improve AI assistants used in help desks, automated support systems, and conversational interfaces for Korean-speaking users. - **Industry:** Language Technology & NLP Development — **Title:** Improving Language Models for Conversational Tasks — **Text:** Developers can apply this dataset to train and evaluate natural language processing models. The diverse dialogues and speakers provide valuable training data for pre-trained models performing conversational tasks, dialogue generation, and context understanding. This data helps improve model performance in Korean conversational AI systems and other language technology applications. ### Fact **FAQs Heading:** FAQs **List of Questions:** - **Question:** What types of annotations are provided in the dataset? — **Answer:** The dataset includes metadata annotations such as file ID, language, and audio format. These labels help organize the dataset and support data analysis, model training, and performance evaluation in speech and natural language processing tasks. - **Question:** How was the data collected? — **Answer:** Data was collected through crowdsourcing platforms, where participants recorded dialogues with AI systems in Korean. - **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets follow a dual-licensing model, where free samples are available for testing and evaluation while the full dataset is accessible through purchase. This structure allows organizations to validate dataset quality before integrating it into production systems. - **Question:** Do Unidata datasets comply with GDPR and other data privacy regulations? — **Answer:** Yes. All datasets are curated in accordance with GDPR and relevant data protection laws, ensuring ethical and lawful data collection. Data sources are verified to maintain responsible use in machine learning, artificial intelligence, and analytics applications. - **Question:** How are Unidata datasets stored? — **Answer:** Unidata datasets are securely stored on AWS cloud infrastructure, providing high availability and scalability. Storage practices comply with ISO 27001 and ISO 27701 standards, ensuring internationally recognized data security and privacy management. - **Question:** How long does it take to receive the dataset? — **Answer:** After submitting a request, the team reviews the dataset requirements and completes the necessary documentation. Once the agreement is signed and payment is processed, the dataset is typically delivered within 3–10 days. - **Question:** Can I request a sample of the dataset before purchasing or downloading it? — **Answer:** Yes, you can request a sample of the dataset for testing and evaluation. Reviewing sample audio files allows teams to verify recording quality, dialogue diversity, and metadata structure before using the dataset. - **Question:** How does human-robot dialogue data improve speech recognition systems? — **Answer:** Human-robot dialogue data exposes AI models to realistic conversational scenarios, helping them recognize spoken language, understand user intent, and improve the accuracy of voice-based interactions. [Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)