ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents
This toolkit enables collaborative research across technical and non-technical domains by providing an easy-to-use platform for developing multi-modal and socially-engaged conversational agents.
The authors introduced ADVISER, an open-source toolkit for building conversational agents that support multiple modalities (speech, text, vision) and social engagement features like emotion recognition, addressing the need for flexible and accessible development platforms.
We present ADVISER - an open-source, multi-domain dialog system toolkit that enables the development of multi-modal (incorporating speech, text and vision), socially-engaged (e.g. emotion recognition, engagement level prediction and backchanneling) conversational agents. The final Python-based implementation of our toolkit is flexible, easy to use, and easy to extend not only for technically experienced users, such as machine learning researchers, but also for less technically experienced users, such as linguists or cognitive scientists, thereby providing a flexible platform for collaborative research. Link to open-source code: https://github.com/DigitalPhonetics/adviser