ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents

• URL: http://arxiv.org/abs/2005.01777v1
• Date: Mon, 4 May 2020 18:27:58 GMT
• Title: ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents
• Authors: Chia-Yu Li, Daniel Ortega, Dirk V\"ath, Florian Lux, Lindsey Vanderlyn, Maximilian Schmidt, Michael Neumann, Moritz V\"olkel, Pavel Denisov, Sabrina Jenne, Zorica Kacarevic and Ngoc Thang Vu
• Abstract summary: ADVISER is an open-source, multi-domain dialog system toolkit. It enables the development of multi-modal (incorporating speech, text and vision) conversational agents. The final Python-based implementation of our toolkit is flexible, easy to use, and easy to extend.
• Score: 27.222054181839095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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