Paper Info
Title
Spoken language understanding for social robotics
Abstract
Speech understanding is a fundamental feature of social robots, since spoken language is the most natural mean of human-human communication. Providing a robot with the ability to understand human language makes it much more accessible to a wide range of users, especially for those who are not experts in the field. Speech understanding is composed of two sub-tasks. The first one is known as automatic speech recognition (ASR), which is the process of translating or transcribing an audio signal into a written text. The second one is natural language understanding (NLU), which consists in obtaining a semantic interpretation from the (previously) transcribed text. In this work, we present a speech-input natural language understanding system for social robots which has been successfully tested with the well-known HuRIC v1.2 corpus obtaining state-of-the art results. Preliminary versions of the proposed system were also tested in real scenarios during the last two editions of the RoCKIn@Home competition, where we were classified in first and second positions respectively.
Year
DOI
Venue
2020
10.1109/ICARSC49921.2020.9096175
2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
Keywords
DocType
ISSN
social robot,speech understanding,human-robot interaction
Conference
2573-9360
ISBN
Citations 
PageRank 
978-1-7281-7079-4
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Cristina Romero-González133.52
Jesus Martinez-gomez2245.76
Ismael García-varea327536.16