--- title: "Human-Robot Conversation Dataset (English)" description: "Human-Robot dataset contains 660+ hours of audio featuring dialogues between AI and a human in English across 20,000 recordings. The dataset supports conversational AI, speech recognition,…" url: "https://unidata.pro/datasets/human-robot-conversation-english/" date_modified: "2026-04-09T13:13:30+03:00" language: "en-US" --- Human-Robot dataset contains 660+ hours of audio featuring dialogues between AI and a human in English across 20,000 recordings. The dataset supports conversational AI, speech recognition, and human-robot interaction research, with short M4A audio files (up to 2 minutes) and structured metadata for model training. ## 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 | Audio of dialogues between AI and humans in English | | Data types | Audio | | Tasks | Speech Recognition, LLM | | Hours of audio | 660+ | | Number of sets | 20,000 | | Language | English | | Labeling | Metadata (id, language, format) | **Media Slider:** - **Video on Slayder:** **Link to the sample:** [Download sample](https://drive.google.com/drive/folders/1xw0l-Vc6Sfa5HReL3aTCm6aAV1o4oVxR) ### Technical Specifications **Table with data:** | Characteristic | Data | | --- | --- | | Audio Format | M4A | | 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 Research — **Title:** Training Natural Human-Robot Dialogue Systems — **Text:** An AI – human conversation dataset with spoken dialogues between humans and artificial agents supports research in human-robot interactions. The audio dataset contains natural conversations recorded in realistic interaction scenarios. Researchers use this dialogue dataset to train language models that help robotic systems understand spoken instructions, respond naturally, and improve human-robot communication tasks. - **Industry:** Artificial Intelligence & Conversational Agents — **Title:** Developing AI Dialogue and Interaction Models — **Text:** Such datasets provide valuable training data for conversational agents and dialogue systems. The dataset contains multiple conversations with annotated speech and interaction context. Machine learning models learn dialogue structure, conversational intent, and response patterns, improving language models used in voice assistants, conversational AI platforms, and interactive artificial agents. - **Industry:** Speech Technology & Language Processing — **Title:** Speech Recognition for Human-Robot Communication — **Text:** This audio dataset supports speech recognition systems designed for human-robot communication. Recordings include natural English speech, conversational exchanges, and varied speaking styles. Models trained on these datasets improve spoken language understanding, speech transcription accuracy, and interaction analysis in robotic systems operating in real-world human environments and collaborative human-robot teams. - **Industry:** Human-Centered AI & Social Robotics — **Title:** Understanding Human Emotions in Robot Interaction — **Text:** Human-robot dialogue datasets help researchers study emotional signals and social context in conversations with artificial agents. Annotated recordings enable emotion recognition and interaction analysis within spoken dialogues. These datasets support machine learning models that allow robots to recognize human emotions, respond appropriately, and function effectively in social human-robot interaction scenarios. ### Fact **FAQs Heading:** FAQs **List of Questions:** - **Question:** What types of annotations are provided in the dataset? — **Answer:** The dataset includes structured metadata annotations such as file ID, language, and audio format. These annotations help organize the dataset and simplify data analysis, enabling efficient preparation of training datasets for speech and dialogue models. - **Question:** Can I request a sample of the Dataset before purchasing? — **Answer:** Yes, a sample of the dataset can be requested before purchasing the full dataset. Reviewing a sample helps researchers examine the audio recordings, dialogue structure, and metadata. - **Question:** What are the sources of data for the dataset? — **Answer:** Data for this conversation dataset was collected through verified crowdsourcing platforms, where participants recorded structured dialogues between humans and AI agents. - **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets follow a dual-licensing model. Free samples are available for testing and validation, while full datasets containing large volumes of conversational audio data are provided through purchase. - **Question:** Do Unidata datasets comply with GDPR and other privacy regulations? — **Answer:** Yes, all datasets are curated in compliance with GDPR and applicable data protection laws. Data is collected from legally permissible sources to ensure ethical use in AI training, machine learning research, and conversational technology development. - **Question:** How are Unidata datasets stored? — **Answer:** All datasets are stored securely on AWS cloud infrastructure, ensuring high availability and scalability. Storage and management practices follow ISO 27001 and ISO 27701 standards, providing a secure environment for managing audio datasets, metadata, and annotations. - **Question:** How long does it take to receive the dataset? — **Answer:** After submitting a request, the Unidata team will review the project requirements and finalize the necessary documentation. Once the agreement is signed and payment is completed, the dataset is typically delivered within 3–10 days. - **Question:** How can human-robot conversation data improve conversational AI systems? — **Answer:** Human-robot conversation data helps AI models learn how people interact with intelligent systems through natural dialogue. It supports the development of more responsive conversational agents, voice assistants, and interactive AI applications. [Full list of this site's AI-readable pages](https://unidata.pro/llms.txt)