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Commercial
Human-Robot Conversation Dataset (German)
Human-robot dataset is an audio dataset comprising 660+ hours of German dialogues between an AI and humans across 20,000 recordings, designed for training conversational agents, speech recognition systems, and language models. The conversation dataset includes short audio sessions (up to 2 minutes) in M4A and WAV formats with structured metadata.
-
- Hours
- 660+
-
- Files
- 20,000
- Voice Assistant
- ASR
- Machine Learning
- Audio Processing
- Voice Recognition
- Voice Assistant
- ASR
- Machine Learning
- Audio Processing
- Voice Recognition
Human-robot dataset is an audio dataset comprising 660+ hours of German dialogues between an AI and humans across 20,000 recordings, designed for training conversational agents, speech recognition systems, and language models. The conversation dataset includes short audio sessions (up to 2 minutes) in M4A and WAV formats with structured metadata.
- Voice Assistant
- ASR
- Machine Learning
- Audio Processing
- Voice Recognition
-
- Hours
- 660+
-
- Files
- 20,000
Dataset Info
| Characteristic | Data |
| Description | Audio of German dialogues between AI and humans |
| Data types | Audio |
| Tasks | Speech Recognition, LLM |
| Hours of audio | 660+ |
| Number of sets | 20,000 |
| Language | German |
| Labeling | Metadata (id, language, format) |
Technical
Characteristics
| Characteristic | Data |
| Audio Format | M4A, WAV |
| Duration | Max = 2 min |
Dataset Use Cases
FAQs
What types of annotations are provided in the dataset?
Each audio file includes structured metadata annotations, including file ID, language, and audio format. These annotations help organize the dataset and support efficient data analysis, training dataset preparation, and model evaluation.
What audio formats and technical characteristics are included?
The dataset includes audio recordings in M4A and WAV formats with a maximum duration of approximately two minutes per dialogue file. These standardized formats make the dataset suitable for speech datasets, language processing pipelines, and conversational AI development.
How was the human-robot conversation data collected?
Data was collected through crowdsourcing platforms where participants recorded dialogues simulating human-robot interactions in the German language. This process enables large-scale data collection with varied conversation patterns and realistic speech scenarios.
How are Unidata datasets licensed?
Unidata datasets follow a dual-licensing model. Free samples are provided for testing and evaluation, while complete datasets are available exclusively through purchase.
Do Unidata datasets comply with GDPR and 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 development, machine learning, and research applications.
How are Unidata datasets stored?
All datasets are securely stored on AWS cloud infrastructure, ensuring high availability and scalability. Data storage and management follow ISO 27001 and ISO 27701 standards, which provide internationally recognized security and privacy protection.
How long does it take to receive the dataset?
After submitting a request, the Unidata team reviews the details and completes the required documentation. Once the agreement is signed and payment is processed, the dataset is delivered within 3–10 days.
How does conversational audio improve the performance of speech and language models?
Conversational audio provides examples of real communication between users and AI systems, helping models learn dialogue flow and spoken language patterns. This improves speech recognition, response generation, and overall interaction quality.
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