714 research outputs found
Algebraic Approach to Physical-Layer Network Coding
The problem of designing physical-layer network coding (PNC) schemes via
nested lattices is considered. Building on the compute-and-forward (C&F)
relaying strategy of Nazer and Gastpar, who demonstrated its asymptotic gain
using information-theoretic tools, an algebraic approach is taken to show its
potential in practical, non-asymptotic, settings. A general framework is
developed for studying nested-lattice-based PNC schemes---called lattice
network coding (LNC) schemes for short---by making a direct connection between
C&F and module theory. In particular, a generic LNC scheme is presented that
makes no assumptions on the underlying nested lattice code. C&F is
re-interpreted in this framework, and several generalized constructions of LNC
schemes are given. The generic LNC scheme naturally leads to a linear network
coding channel over modules, based on which non-coherent network coding can be
achieved. Next, performance/complexity tradeoffs of LNC schemes are studied,
with a particular focus on hypercube-shaped LNC schemes. The error probability
of this class of LNC schemes is largely determined by the minimum inter-coset
distances of the underlying nested lattice code. Several illustrative
hypercube-shaped LNC schemes are designed based on Construction A and D,
showing that nominal coding gains of 3 to 7.5 dB can be obtained with
reasonable decoding complexity. Finally, the possibility of decoding multiple
linear combinations is considered and related to the shortest independent
vectors problem. A notion of dominant solutions is developed together with a
suitable lattice-reduction-based algorithm.Comment: Submitted to IEEE Transactions on Information Theory, July 21, 2011.
Revised version submitted Sept. 17, 2012. Final version submitted July 3,
201
ASR error management for improving spoken language understanding
This paper addresses the problem of automatic speech recognition (ASR) error
detection and their use for improving spoken language understanding (SLU)
systems. In this study, the SLU task consists in automatically extracting, from
ASR transcriptions , semantic concepts and concept/values pairs in a e.g
touristic information system. An approach is proposed for enriching the set of
semantic labels with error specific labels and by using a recently proposed
neural approach based on word embeddings to compute well calibrated ASR
confidence measures. Experimental results are reported showing that it is
possible to decrease significantly the Concept/Value Error Rate with a state of
the art system, outperforming previously published results performance on the
same experimental data. It also shown that combining an SLU approach based on
conditional random fields with a neural encoder/decoder attention based
architecture , it is possible to effectively identifying confidence islands and
uncertain semantic output segments useful for deciding appropriate error
handling actions by the dialogue manager strategy .Comment: Interspeech 2017, Aug 2017, Stockholm, Sweden. 201
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