6,410 research outputs found
Multimodal Grounding for Sequence-to-Sequence Speech Recognition
Humans are capable of processing speech by making use of multiple sensory
modalities. For example, the environment where a conversation takes place
generally provides semantic and/or acoustic context that helps us to resolve
ambiguities or to recall named entities. Motivated by this, there have been
many works studying the integration of visual information into the speech
recognition pipeline. Specifically, in our previous work, we propose a
multistep visual adaptive training approach which improves the accuracy of an
audio-based Automatic Speech Recognition (ASR) system. This approach, however,
is not end-to-end as it requires fine-tuning the whole model with an adaptation
layer. In this paper, we propose novel end-to-end multimodal ASR systems and
compare them to the adaptive approach by using a range of visual
representations obtained from state-of-the-art convolutional neural networks.
We show that adaptive training is effective for S2S models leading to an
absolute improvement of 1.4% in word error rate. As for the end-to-end systems,
although they perform better than baseline, the improvements are slightly less
than adaptive training, 0.8 absolute WER reduction in single-best models. Using
ensemble decoding, end-to-end models reach a WER of 15% which is the lowest
score among all systems.Comment: ICASSP 201
Multimodal Grounding for Language Processing
This survey discusses how recent developments in multimodal processing
facilitate conceptual grounding of language. We categorize the information flow
in multimodal processing with respect to cognitive models of human information
processing and analyze different methods for combining multimodal
representations. Based on this methodological inventory, we discuss the benefit
of multimodal grounding for a variety of language processing tasks and the
challenges that arise. We particularly focus on multimodal grounding of verbs
which play a crucial role for the compositional power of language.Comment: The paper has been published in the Proceedings of the 27 Conference
of Computational Linguistics. Please refer to this version for citations:
https://www.aclweb.org/anthology/papers/C/C18/C18-1197
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Conjunctive Visual and Auditory Development via Real-Time Dialogue
Human developmental learning is capable of
dealing with the dynamic visual world, speech-based
dialogue, and their complex real-time association.
However, the architecture that realizes
this for robotic cognitive development has
not been reported in the past. This paper takes
up this challenge. The proposed architecture does
not require a strict coupling between visual and
auditory stimuli. Two major operations contribute
to the “abstraction” process: multiscale temporal
priming and high-dimensional numeric abstraction
through internal responses with reduced variance.
As a basic principle of developmental learning,
the programmer does not know the nature
of the world events at the time of programming
and, thus, hand-designed task-specific representation
is not possible. We successfully tested the
architecture on the SAIL robot under an unprecedented
challenging multimodal interaction mode:
use real-time speech dialogue as a teaching source
for simultaneous and incremental visual learning
and language acquisition, while the robot is viewing
a dynamic world that contains a rotating object
to which the dialogue is referring
Object Referring in Videos with Language and Human Gaze
We investigate the problem of object referring (OR) i.e. to localize a target
object in a visual scene coming with a language description. Humans perceive
the world more as continued video snippets than as static images, and describe
objects not only by their appearance, but also by their spatio-temporal context
and motion features. Humans also gaze at the object when they issue a referring
expression. Existing works for OR mostly focus on static images only, which
fall short in providing many such cues. This paper addresses OR in videos with
language and human gaze. To that end, we present a new video dataset for OR,
with 30, 000 objects over 5, 000 stereo video sequences annotated for their
descriptions and gaze. We further propose a novel network model for OR in
videos, by integrating appearance, motion, gaze, and spatio-temporal context
into one network. Experimental results show that our method effectively
utilizes motion cues, human gaze, and spatio-temporal context. Our method
outperforms previousOR methods. For dataset and code, please refer
https://people.ee.ethz.ch/~arunv/ORGaze.html.Comment: Accepted to CVPR 2018, 10 pages, 6 figure
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