How do we teach our AI sign language?
The development of a sign language avatar requires more than technical implementation and quality assurance. A key part of the process is enabling artificial intelligence (AI) to understand and use German Sign Language (DGS). To achieve this, the method of annotation is applied.
The Principle of Annotation
Annotation refers to the precise recording and documentation of linguistic features. For sign language, this means linking a large amount of animation data with detailed descriptions of the performed sign. Various parameters are taken into account, such as:
- Handshape
- Hand orientation
- Spatial placement
- Movement
- Facial expression
Together, these elements form the foundation of a sign’s meaning.
The AI Learning Process
Through annotation, data is structured in a way that allows AI to recognize patterns and derive meanings. Each recorded feature is systematically linked to words or concepts. In this way, the AI gradually develops an understanding of sign language.
The process is similar to language acquisition: by repeatedly associating visual features with linguistic meanings, the AI progressively builds its own set of rules.
Perspective
The long-term goal is to create an avatar that not only understands sign language but can also perform it. This will enable direct interaction in DGS with a digital avatar.
Conclusion
Annotation is a key building block in training AI for sign language. By precisely describing handshapes, movements, and facial expressions, the foundation for a deeper understanding of the language is created. Only through this step does it become possible to develop an avatar that can understand DGS and, in the future, actively participate in communication.
This glimpse only shows part of our work – we’d be happy to answer your questions or share more about the process. Follow our Instagram channel @gebaerdensprachavatar to stay up to date!
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