8,639 research outputs found
Visual units and confusion modelling for automatic lip-reading
Automatic lip-reading (ALR) is a challenging task because the visual speech signal is known to be missing some important information, such as voicing. We propose an approach to ALR that acknowledges that this information is missing but assumes that it is substituted or deleted in a systematic way that can be modelled. We describe a system that learns such a model and then incorporates it into decoding, which is realised as a cascade of weighted finite-state transducers. Our results show a small but statistically significant improvement in recognition accuracy. We also investigate the issue of suitable visual units for ALR, and show that visemes are sub-optimal, not but because they introduce lexical ambiguity, but because the reduction in modelling units entailed by their use reduces accuracy
On the Disambiguation of Weighted Automata
We present a disambiguation algorithm for weighted automata. The algorithm
admits two main stages: a pre-disambiguation stage followed by a transition
removal stage. We give a detailed description of the algorithm and the proof of
its correctness. The algorithm is not applicable to all weighted automata but
we prove sufficient conditions for its applicability in the case of the
tropical semiring by introducing the *weak twins property*. In particular, the
algorithm can be used with all acyclic weighted automata, relevant to
applications. While disambiguation can sometimes be achieved using
determinization, our disambiguation algorithm in some cases can return a result
that is exponentially smaller than any equivalent deterministic automaton. We
also present some empirical evidence of the space benefits of disambiguation
over determinization in speech recognition and machine translation
applications
Regular Combinators for String Transformations
We focus on (partial) functions that map input strings to a monoid such as
the set of integers with addition and the set of output strings with
concatenation. The notion of regularity for such functions has been defined
using two-way finite-state transducers, (one-way) cost register automata, and
MSO-definable graph transformations. In this paper, we give an algebraic and
machine-independent characterization of this class analogous to the definition
of regular languages by regular expressions. When the monoid is commutative, we
prove that every regular function can be constructed from constant functions
using the combinators of choice, split sum, and iterated sum, that are analogs
of union, concatenation, and Kleene-*, respectively, but enforce unique (or
unambiguous) parsing. Our main result is for the general case of
non-commutative monoids, which is of particular interest for capturing regular
string-to-string transformations for document processing. We prove that the
following additional combinators suffice for constructing all regular
functions: (1) the left-additive versions of split sum and iterated sum, which
allow transformations such as string reversal; (2) sum of functions, which
allows transformations such as copying of strings; and (3) function
composition, or alternatively, a new concept of chained sum, which allows
output values from adjacent blocks to mix.Comment: This is the full version, with omitted proofs and constructions, of
the conference paper currently in submissio
Damage localization map using electromechanical impedance spectrums and inverse distance weighting interpolation: Experimental validation on thin composite structures
Piezoelectric sensors are widely used for structure health monitoring technique. In particular, electromechanical impedance techniques give simple and low-cost solutions for detecting damage in composite structures. The purpose of the method proposed in this article is to generate a damage localization map based on both indicators computed from electromechanical impedance spectrums and inverse distance weighting interpolation. The weights for the interpolation have a physical sense and are computed according to an exponential law of the measured attenuation of acoustic waves. One of the main advantages of the method, so-called data-driven method, is that only experimental data are used as inputs for our algorithm. It does not rely on any model. The proposed method has been validated on both one-dimensional and two-dimensional composite structures
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