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ID int64 2 7 | Language stringclasses 1
value | Format stringclasses 1
value |
|---|---|---|
2 | English | m4a |
3 | English | m4a |
4 | English | m4a |
5 | English | m4a |
6 | English | m4a |
7 | English | m4a |
Human-Robot Dataset
The dataset comprises 660+ hours of English speech across 20,000+ audio files featuring human-robot interactions between AI and humans. It is designed for research in conversational agents, focusing on various speech recognition methods, primarily aimed at advancing language models and machine learning applications.
By utilizing this dataset, researchers and developers can advance their understanding and capabilities in robotic systems and conversational AI technologies. - Get the data
Each recording features natural conversations between humans and robots, simulating potential real-world human-robot interaction scenarios in robotic systems.
Frequently Asked Questions
What is this dataset used for?
This dataset is designed to train and evaluate conversational AI, automatic speech recognition (ASR), and spoken dialogue systems. Researchers can use it to develop models that understand natural conversations between humans and robotic assistants, making it suitable for voice-controlled robots, virtual assistants, and interactive AI applications.
How was the data collected?
The recordings were collected through crowdsourcing, allowing conversations to be gathered from a diverse group of speakers. This collection methodology helps capture natural speech variability while maintaining consistent recording quality, resulting in data that better reflects real-world conversational scenarios.
What metadata accompanies the recordings?
Each audio file includes metadata such as file ID, language, and audio format.
💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.
Researchers can utilize this dataset to explore detection technology and recognition algorithms that aim to understand how robots should interact with humans and improve conversational authentication processes.
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