1,694 research outputs found
Complexity Analysis of Reed-Solomon Decoding over GF(2^m) Without Using Syndromes
For the majority of the applications of Reed-Solomon (RS) codes, hard
decision decoding is based on syndromes. Recently, there has been renewed
interest in decoding RS codes without using syndromes. In this paper, we
investigate the complexity of syndromeless decoding for RS codes, and compare
it to that of syndrome-based decoding. Aiming to provide guidelines to
practical applications, our complexity analysis differs in several aspects from
existing asymptotic complexity analysis, which is typically based on
multiplicative fast Fourier transform (FFT) techniques and is usually in big O
notation. First, we focus on RS codes over characteristic-2 fields, over which
some multiplicative FFT techniques are not applicable. Secondly, due to
moderate block lengths of RS codes in practice, our analysis is complete since
all terms in the complexities are accounted for. Finally, in addition to fast
implementation using additive FFT techniques, we also consider direct
implementation, which is still relevant for RS codes with moderate lengths.
Comparing the complexities of both syndromeless and syndrome-based decoding
algorithms based on direct and fast implementations, we show that syndromeless
decoding algorithms have higher complexities than syndrome-based ones for high
rate RS codes regardless of the implementation. Both errors-only and
errors-and-erasures decoding are considered in this paper. We also derive
tighter bounds on the complexities of fast polynomial multiplications based on
Cantor's approach and the fast extended Euclidean algorithm.Comment: 11 pages, submitted to EURASIP Journal on Wireless Communications and
Networkin
Phonetic Temporal Neural Model for Language Identification
Deep neural models, particularly the LSTM-RNN model, have shown great
potential for language identification (LID). However, the use of phonetic
information has been largely overlooked by most existing neural LID methods,
although this information has been used very successfully in conventional
phonetic LID systems. We present a phonetic temporal neural model for LID,
which is an LSTM-RNN LID system that accepts phonetic features produced by a
phone-discriminative DNN as the input, rather than raw acoustic features. This
new model is similar to traditional phonetic LID methods, but the phonetic
knowledge here is much richer: it is at the frame level and involves compacted
information of all phones. Our experiments conducted on the Babel database and
the AP16-OLR database demonstrate that the temporal phonetic neural approach is
very effective, and significantly outperforms existing acoustic neural models.
It also outperforms the conventional i-vector approach on short utterances and
in noisy conditions.Comment: Submitted to TASL
Phone-aware Neural Language Identification
Pure acoustic neural models, particularly the LSTM-RNN model, have shown
great potential in language identification (LID). However, the phonetic
information has been largely overlooked by most of existing neural LID models,
although this information has been used in the conventional phonetic LID
systems with a great success. We present a phone-aware neural LID architecture,
which is a deep LSTM-RNN LID system but accepts output from an RNN-based ASR
system. By utilizing the phonetic knowledge, the LID performance can be
significantly improved. Interestingly, even if the test language is not
involved in the ASR training, the phonetic knowledge still presents a large
contribution. Our experiments conducted on four languages within the Babel
corpus demonstrated that the phone-aware approach is highly effective.Comment: arXiv admin note: text overlap with arXiv:1705.0315
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