5 accepted papers
In medical laboratory environments, where pre-cision and safety are critical, the deployment of autonomous robots requires not only accurate object manipulation but also the ability to verify task success to comply with regulatory requirements. This paper introduces a novel imagination-enabled perce
In our previous work, we designed a human-like white-box and causal generative model of perception NaivPhys4RP, essentially based on cognitive emulation to understand the past, the present and the future of the state of complex worlds from poor observations. In this paper, as recommended in that pre
Many of today’s robot perception systems aim at accomplishing perception tasks that are too simplistic and too hard. They are too simplistic because they do not require the perception systems to provide all the information needed to accomplish manipulation tasks. Typically the perception results do…
In this paper we present our findings on how representation learning on large unlabeled speech corpora can be beneficially utilized for speech emotion recognition (SER). Prior work on representation learning for SER mostly focused on the relatively small emotional speech datasets without making use…
Research on multilingual speech emotion recognition faces the problem that most available speech corpora differ from each other in important ways, such as annotation methods or interaction scenarios. These inconsistencies complicate building a multilingual system. We present results for cross-lingua…