39 research outputs found
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model
Multilingual models for Automatic Speech Recognition (ASR) are attractive as
they have been shown to benefit from more training data, and better lend
themselves to adaptation to under-resourced languages. However, initialisation
from monolingual context-dependent models leads to an explosion of
context-dependent states. Connectionist Temporal Classification (CTC) is a
potential solution to this as it performs well with monophone labels.
We investigate multilingual CTC in the context of adaptation and
regularisation techniques that have been shown to be beneficial in more
conventional contexts. The multilingual model is trained to model a universal
International Phonetic Alphabet (IPA)-based phone set using the CTC loss
function. Learning Hidden Unit Contribution (LHUC) is investigated to perform
language adaptive training. In addition, dropout during cross-lingual
adaptation is also studied and tested in order to mitigate the overfitting
problem.
Experiments show that the performance of the universal phoneme-based CTC
system can be improved by applying LHUC and it is extensible to new phonemes
during cross-lingual adaptation. Updating all the parameters shows consistent
improvement on limited data. Applying dropout during adaptation can further
improve the system and achieve competitive performance with Deep Neural Network
/ Hidden Markov Model (DNN/HMM) systems on limited data
A Bayesian Approach to Recurrence in Neural Networks
We begin by reiterating that common neural network activation functions have
simple Bayesian origins. In this spirit, we go on to show that Bayes's theorem
also implies a simple recurrence relation; this leads to a Bayesian recurrent
unit with a prescribed feedback formulation. We show that introduction of a
context indicator leads to a variable feedback that is similar to the forget
mechanism in conventional recurrent units. A similar approach leads to a
probabilistic input gate. The Bayesian formulation leads naturally to the two
pass algorithm of the Kalman smoother or forward-backward algorithm, meaning
that inference naturally depends upon future inputs as well as past ones.
Experiments on speech recognition confirm that the resulting architecture can
perform as well as a bidirectional recurrent network with the same number of
parameters as a unidirectional one. Further, when configured explicitly
bidirectionally, the architecture can exceed the performance of a conventional
bidirectional recurrence
Nuclear Matter and Neutron Stars from Relativistic Brueckner-Hartree-Fock Theory
The momentum and isospin dependence of the single-particle potential for the
in-medium nucleon are the key quantities in the Relativistic
Brueckner-Hartree-Fock (RBHF) theory. It depends on how to extract the scalar
and the vector components of the single-particle potential inside nuclear
matter. In contrast to the RBHF calculations in the Dirac space with the
positive-energy states (PESs) only, the single-particle potential can be
determined in a unique way by the RBHF theory together with the negative-energy
states (NESs), i.e., the RBHF theory in the full Dirac space. The saturation
properties of symmetric and asymmetric nuclear matter in the full Dirac space
are systematically investigated based on the realistic Bonn nucleon-nucleon
potentials. In order to further specify the importance of the calculations in
the full Dirac space, the neutron star properties are investigated. The direct
URCA process in neutron star cooling will happen at density
fm with the proton fractions
. The radii of a neutron star are
predicated as km, and their tidal
deformabilities are for potential Bonn A,
B, C. Comparing with the results obtained in the Dirac space with PESs only,
full-Dirac-space RBHF calculation predicts the softest symmetry energy which
would be more favored by the gravitational waves (GW) detection from GW170817.
Furthermore, the results from full-Dirac-space RBHF theory are consistent with
the recent astronomical observations of massive neutron stars and simultaneous
mass-radius measurement
Theoretical Puncture Mechanics of Soft Compressible Solids
Accurate prediction of the force required to puncture a soft material is
critical in many fields like medical technology, food processing, and
manufacturing. However, such a prediction strongly depends on our understanding
of the complex nonlinear behavior of the material subject to deep indentation
and complex failure mechanisms. Only recently we developed theories capable of
correlating puncture force with material properties and needle geometry.
However, such models are based on simplifications that seldom limit their
applicability to real cases. One common assumption is the incompressibility of
the cut material, albeit no material is truly incompressible. In this paper we
propose a simple model that accounts for linearly elastic compressibility, and
its interplay with toughness, stiffness, and elastic strain-stiffening.
Confirming previous theories and experiments, materials having high-toughness
and low-modulus exhibit the highest puncture resistance at a given needle
radius. Surprisingly, in these conditions, we observe that incompressible
materials exhibit the lowest puncture resistance, where volumetric
compressibility can create an additional (strain) energy barrier to puncture.
Our model provides a valuable tool to assess the puncture resistance of soft
compressible materials and suggests new design strategies for sharp needles and
puncture-resistant materials
Properties of Pb predicted from the relativistic equation of state in the full Dirac space
Relativistic Brueckner-Hartree-Fock (RBHF) theory in the full Dirac space
allows one to determine uniquely the momentum dependence of scalar and vector
components of the single-particle potentials. In order to extend this new
method from nuclear matter to finite nuclei, as a first step, properties of
Pb are explored by using the microscopic equation of state for
asymmetric nuclear matter and a liquid droplet model. The neutron and proton
density distributions, the binding energies, the neutron and proton radii, and
the neutron skin thickness in Pb are calculated. In order to further
compare the charge densities predicted from the RBHF theory in the full Dirac
space with the experimental charge densities, the differential cross sections
and the electric charge form factors in the elastic electron-nucleus scattering
are obtained by using the phase-shift analysis method. The results from the
RBHF theory are in good agreement with the experimental data. In addition, the
uncertainty arising from variations of the surface term parameter in the
liquid droplet model is also discussed
Isospin splitting of the Dirac mass probed by the relativistic Brueckner-Hartree-Fock theory in the full Dirac space
The isospin splitting of the Dirac mass obtained with the relativistic
Brueckner-Hartree-Fock (RBHF) theory is thoroughly investigated. From the
perspective in the full Dirac space, the long-standing controversy between the
momentum-independence approximation (MIA) method and the projection method on
the isospin splitting of the Dirac mass in asymmetric nuclear matter (ANM) is
analyzed in detail. We find that, the \textit{assumption procedure} of the MIA
method, which assumes that the single-particle potentials are momentum
independent, is not a sufficient condition that directly leads to the wrong
sign of the isospin splitting of the Dirac mass, while the \textit{extraction
procedure} of the MIA method, which extracts the single-particle potentials
from the single-particle potential energy, leads to the wrong sign. By
approximately solving the set of equations involved in the \textit{extraction
procedure}, a formal expression of the Dirac mass is obtained. The wrong
isospin splitting of the Dirac mass is mainly caused by that the
\textit{extraction procedure} forcely assumes the momentum dependence of the
single-particle potential energy to be a quadratic form where the strength is
solely determined by the constant scalar potential.Comment: 13 pages, 4 figure
Neutron-proton effective mass splitting in neutron-rich matter
Nucleon effective masses in neutron-rich matter are studied with the
relativistic Brueckner-Hartree-Fock (RBHF) theory in the full Dirac space. The
neutron and proton effective masses for symmetric nuclear matter are 0.80,
which agrees well with the empirical values. In neutron-rich matter, the
effective mass of the neutron is found larger than that of the proton, and the
neutron-proton effective mass splittings at the empirical saturation density
are predicted as with being the isospin asymmetry
parameter. The result is compared to other ab initio calculations and is
consistent with the constraints from the nuclear reaction and structure
measurements, such as the nucleon-nucleus scattering, the giant resonances of
Pb, and the Hugenholtz-Van Hove theorem with systematics of nuclear
symmetry energy and its slope. The predictions of the neutron-proton effective
mass splitting from the RBHF theory in the full Dirac space might be helpful to
constrain the isovector parameters in phenomenological density functionals.Comment: 14 pages, 4 figure
An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation
Different training and adaptation techniques for multilingual Automatic Speech Recognition (ASR) are explored in the context of hybrid systems, exploiting Deep Neural Networks (DNN) and Hidden Markov Models (HMM). In multilingual DNN training, the hidden layers (possibly extracting bottleneck features) are usually shared across languages, and the output layer can either model multiple sets of language-specific senones or one single universal IPA-based multilingual senone set. Both architectures are investigated, exploiting and comparing different language adaptive training (LAT) techniques originating from successful DNN-based speaker-adaptation. More specifically, speaker adaptive training methods such as Cluster Adaptive Training (CAT) and Learning Hidden Unit Contribution (LHUC) are considered. In addition, a language adaptive output architecture for IPA-based universal DNN is also studied and tested. Experiments show that LAT improves the performance and adaptation on the top layer further improves the accuracy. By combining state-level minimum Bayes risk (sMBR) sequence training with LAT, we show that a language adaptively trained IPA-based universal DNN outperforms a monolingually sequence trained model
The SUMMA Platform Prototype
We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams