48,664 research outputs found
Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction
We develop a natural language interface for human robot interaction that
implements reasoning about deep semantics in natural language. To realize the
required deep analysis, we employ methods from cognitive linguistics, namely
the modular and compositional framework of Embodied Construction Grammar (ECG)
[Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference
resolution problems and other issues related to deep semantics and
compositionality of natural language. This also includes verbal interaction
with humans to clarify commands and queries that are too ambiguous to be
executed safely. We implement our NLU framework as a ROS package and present
proof-of-concept scenarios with different robots, as well as a survey on the
state of the art
Listening to the World Improves Speech Command Recognition
We study transfer learning in convolutional network architectures applied to
the task of recognizing audio, such as environmental sound events and speech
commands. Our key finding is that not only is it possible to transfer
representations from an unrelated task like environmental sound classification
to a voice-focused task like speech command recognition, but also that doing so
improves accuracies significantly. We also investigate the effect of increased
model capacity for transfer learning audio, by first validating known results
from the field of Computer Vision of achieving better accuracies with
increasingly deeper networks on two audio datasets: UrbanSound8k and the newly
released Google Speech Commands dataset. Then we propose a simple multiscale
input representation using dilated convolutions and show that it is able to
aggregate larger contexts and increase classification performance. Further, the
models trained using a combination of transfer learning and multiscale input
representations need only 40% of the training data to achieve similar
accuracies as a freshly trained model with 100% of the training data. Finally,
we demonstrate a positive interaction effect for the multiscale input and
transfer learning, making a case for the joint application of the two
techniques.Comment: 8 page
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