724 research outputs found
Towards Subject Independent Sign Language Recognition : A Segment-Based Probabilistic Approach
Ph.DDOCTOR OF PHILOSOPH
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
A New Re-synchronization Method based Multi-modal Fusion for Automatic Continuous Cued Speech Recognition
Cued Speech (CS) is an augmented lip reading complemented by hand coding, and it is very helpful to the deaf people. Automatic CS recognition can help communications between the deaf people and others. Due to the asynchronous nature of lips and hand movements, fusion of them in automatic CS recognition is a challenging problem. In this work, we propose a novel re-synchronization procedure for multi-modal fusion, which aligns the hand features with lips feature. It is realized by delaying hand position and hand shape with their optimal hand preceding time which is derived by investigating the temporal organizations of hand position and hand shape movements in CS. This re-synchronization procedure is incorporated into a practical continuous CS recognition system that combines convolutional neural network (CNN) with multi-stream hidden markov model (MSHMM). A significant improvement of about 4.6% has been achieved retaining 76.6% CS phoneme recognition correctness compared with the state-of-the-art architecture (72.04%), which did not take into account the asynchrony issue of multi-modal fusion in CS. To our knowledge, this is the first work to tackle the asynchronous multi-modal fusion in the automatic continuous CS recognition
Recognizing Speech in a Novel Accent: The Motor Theory of Speech Perception Reframed
The motor theory of speech perception holds that we perceive the speech of
another in terms of a motor representation of that speech. However, when we
have learned to recognize a foreign accent, it seems plausible that recognition
of a word rarely involves reconstruction of the speech gestures of the speaker
rather than the listener. To better assess the motor theory and this
observation, we proceed in three stages. Part 1 places the motor theory of
speech perception in a larger framework based on our earlier models of the
adaptive formation of mirror neurons for grasping, and for viewing extensions
of that mirror system as part of a larger system for neuro-linguistic
processing, augmented by the present consideration of recognizing speech in a
novel accent. Part 2 then offers a novel computational model of how a listener
comes to understand the speech of someone speaking the listener's native
language with a foreign accent. The core tenet of the model is that the
listener uses hypotheses about the word the speaker is currently uttering to
update probabilities linking the sound produced by the speaker to phonemes in
the native language repertoire of the listener. This, on average, improves the
recognition of later words. This model is neutral regarding the nature of the
representations it uses (motor vs. auditory). It serve as a reference point for
the discussion in Part 3, which proposes a dual-stream neuro-linguistic
architecture to revisits claims for and against the motor theory of speech
perception and the relevance of mirror neurons, and extracts some implications
for the reframing of the motor theory
Data mining and modelling for sign language
Sign languages have received significantly less attention than spoken languages in the
research areas of corpus analysis, machine translation, recognition, synthesis and social signal processing, amongst others. This is mainly due to signers being in a clear minority and
there being a strong prior belief that sign languages are simply arbitrary gestures. To date,
this manifests in the insufficiency of sign language resources available for computational
modelling and analysis, with no agreed standards and relatively stagnated advancements
compared to spoken language interaction research. Fortunately, the machine learning community has developed methods, such as transfer learning, for dealing with sparse resources,
while data mining techniques, such as clustering can provide insights into the data. The
work described here utilises such transfer learning techniques to apply neural language
model to signed utterances and to compare sign language phonemes, which allows for
clustering of similar signs, leading to automated annotation of sign language resources.
This thesis promotes the idea that sign language research in computing should rely less on
hand-annotated data thus opening up the prospect of using readily available online data
(e.g. signed song videos) through the computational modelling and automated annotation
techniques presented in this thesis
Visual recognition of American sign language using hidden Markov models
Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1995.Includes bibliographical references (leaves 48-52).by Thad Eugene Starner.M.S
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