Commercial

Human-Robot Conversation Dataset (English)

Human-Robot Conversation Dataset (English) 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.

Get in touch Download sample
  • Hours
    660+
  • Files
    20,000
  • Voice Assistant
  • ASR
  • Machine learning
  • Audio Processing
  • Voice Recognition

Human-Robot Conversation Dataset (English) 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.

Get in touch Download sample
  • Voice Assistant
  • ASR
  • Machine learning
  • Audio Processing
  • Voice Recognition
  • Hours
    660+
  • Files
    20,000

Dataset Info

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)
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Technical
Characteristics

Characteristic Data
Audio Format M4A
Duration Max = 2 min
Source and collection methodology: Data was collected via crowdsourcing platforms.

Dataset Use Cases

  • Robotics & Human–Robot Interaction Research

    Training Natural Human-Robot Dialogue Systems

    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.

  • Artificial Intelligence & Conversational Agents

    Developing AI Dialogue and Interaction Models

    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.

  • Speech Technology & Language Processing

    Speech Recognition for Human-Robot Communication

    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.

  • Human-Centered AI & Social Robotics

    Understanding Human Emotions in Robot Interaction

    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.

FAQs

What is included in Human-Robot Conversation Dataset?
The dataset consists of more than 660 hours of English speech recordings distributed across approximately 20,000 dialogue files. Each file contains spoken interactions between humans and AI agents.
What types of annotations are provided in the dataset?
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.
Can I request a sample of the Dataset before purchasing?
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.
What are the sources of data for the dataset?
Data for this conversation dataset was collected through verified crowdsourcing platforms, where participants recorded structured dialogues between humans and AI agents.
How are Unidata datasets licensed?
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.
Do Unidata datasets comply with GDPR and other privacy regulations?
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.
How are Unidata datasets stored?
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.
How long does it take to receive the dataset?
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.
Still have questions about using Unidata datasets? Read our user-guides

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