22,250 research outputs found
Statistically motivated example-based machine translation using translation memory
In this paper we present a novel way of integrating Translation Memory into an Example-based Machine translation System (EBMT) to deal with the issue of low
resources. We have used a dialogue of 380 sentences as the example-base for our system. The translation units in the
Translation Memories are automatically extracted based on the aligned phrases (words) of a statistical machine translation (SMT) system. We attempt to use the approach to improve translation from English to Bangla as many statistical machine translation systems have difficulty
with such small amounts of training data. We have found the approach shows improvement over a baseline SMT system
Simultaneous Hand Pose and Skeleton Bone-Lengths Estimation from a Single Depth Image
Articulated hand pose estimation is a challenging task for human-computer
interaction. The state-of-the-art hand pose estimation algorithms work only
with one or a few subjects for which they have been calibrated or trained.
Particularly, the hybrid methods based on learning followed by model fitting or
model based deep learning do not explicitly consider varying hand shapes and
sizes. In this work, we introduce a novel hybrid algorithm for estimating the
3D hand pose as well as bone-lengths of the hand skeleton at the same time,
from a single depth image. The proposed CNN architecture learns hand pose
parameters and scale parameters associated with the bone-lengths
simultaneously. Subsequently, a new hybrid forward kinematics layer employs
both parameters to estimate 3D joint positions of the hand. For end-to-end
training, we combine three public datasets NYU, ICVL and MSRA-2015 in one
unified format to achieve large variation in hand shapes and sizes. Among
hybrid methods, our method shows improved accuracy over the state-of-the-art on
the combined dataset and the ICVL dataset that contain multiple subjects. Also,
our algorithm is demonstrated to work well with unseen images.Comment: This paper has been accepted and presented in 3DV-2017 conference
held at Qingdao, China. http://irc.cs.sdu.edu.cn/3dv
GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB
We address the highly challenging problem of real-time 3D hand tracking based
on a monocular RGB-only sequence. Our tracking method combines a convolutional
neural network with a kinematic 3D hand model, such that it generalizes well to
unseen data, is robust to occlusions and varying camera viewpoints, and leads
to anatomically plausible as well as temporally smooth hand motions. For
training our CNN we propose a novel approach for the synthetic generation of
training data that is based on a geometrically consistent image-to-image
translation network. To be more specific, we use a neural network that
translates synthetic images to "real" images, such that the so-generated images
follow the same statistical distribution as real-world hand images. For
training this translation network we combine an adversarial loss and a
cycle-consistency loss with a geometric consistency loss in order to preserve
geometric properties (such as hand pose) during translation. We demonstrate
that our hand tracking system outperforms the current state-of-the-art on
challenging RGB-only footage
Automatic Quality Estimation for ASR System Combination
Recognizer Output Voting Error Reduction (ROVER) has been widely used for
system combination in automatic speech recognition (ASR). In order to select
the most appropriate words to insert at each position in the output
transcriptions, some ROVER extensions rely on critical information such as
confidence scores and other ASR decoder features. This information, which is
not always available, highly depends on the decoding process and sometimes
tends to over estimate the real quality of the recognized words. In this paper
we propose a novel variant of ROVER that takes advantage of ASR quality
estimation (QE) for ranking the transcriptions at "segment level" instead of:
i) relying on confidence scores, or ii) feeding ROVER with randomly ordered
hypotheses. We first introduce an effective set of features to compensate for
the absence of ASR decoder information. Then, we apply QE techniques to perform
accurate hypothesis ranking at segment-level before starting the fusion
process. The evaluation is carried out on two different tasks, in which we
respectively combine hypotheses coming from independent ASR systems and
multi-microphone recordings. In both tasks, it is assumed that the ASR decoder
information is not available. The proposed approach significantly outperforms
standard ROVER and it is competitive with two strong oracles that e xploit
prior knowledge about the real quality of the hypotheses to be combined.
Compared to standard ROVER, the abs olute WER improvements in the two
evaluation scenarios range from 0.5% to 7.3%
Coupling Non-Gravitational Fields with Simplicial Spacetimes
The inclusion of source terms in discrete gravity is a long-standing problem.
Providing a consistent coupling of source to the lattice in Regge Calculus (RC)
yields a robust unstructured spacetime mesh applicable to both numerical
relativity and quantum gravity. RC provides a particularly insightful approach
to this problem with its purely geometric representation of spacetime. The
simplicial building blocks of RC enable us to represent all matter and fields
in a coordinate-free manner. We provide an interpretation of RC as a discrete
exterior calculus framework into which non-gravitational fields naturally
couple with the simplicial lattice. Using this approach we obtain a consistent
mapping of the continuum action for non-gravitational fields to the Regge
lattice. In this paper we apply this framework to scalar, vector and tensor
fields. In particular we reconstruct the lattice action for (1) the scalar
field, (2) Maxwell field tensor and (3) Dirac particles. The straightforward
application of our discretization techniques to these three fields demonstrates
a universal implementation of coupling source to the lattice in Regge calculus.Comment: 10 pages, no figures, Latex, fixed typos and minor corrections
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