This paper addresses the issue of robust and joint source-channel decoding of arithmetic codes. We first analyze dependencies between the variables involved in arithmetic coding by means of the Bayesian formalism. This provides a suitable framework for designing a soft decoding algorithm that provides high error-resilience. It also provides a natural setting for "soft synchronization", i.e., to...

In this paper we propose a unified framework for decoding and feature representation based on the Maximum A Posterior (MAP) principle. The search space is augmented with an additional feature stream dimension such that different feature representations can be utilized for different phonetic context under the HMM decoding framework. We also provide a theoretic explanation for the unified framewo...

Minimum Error Rate Training (MERT) and Minimum Bayes-Risk (MBR) decoding are used in most current state-of-theart Statistical Machine Translation (SMT) systems. The algorithms were originally developed to work with N -best lists of translations, and recently extended to lattices that encode many more hypotheses than typical N -best lists. We here extend lattice-based MERT and MBR algorithms to ...

Discriminative translation models utilizing source context have been shown to help statistical machine translation performance. We propose a novel extension of this work using target context information. Surprisingly, we show that this model can be efficiently integrated directly in the decoding process. Our approach scales to large training data sizes and results in consistent improvements in ...

Multiple Input Multiple Output (MIMO) systems have been extensively studied in context of wireless communication system, which promising the both increased capacity and link level reliability. In this paper we will present an analysis of the Vertical Bell Laboratories Layered Space-Time (V-BLAST) system at high signalto noise ratio (SNR) region using BPSK, QPSK and 16-QAM modulation technique b...

A fast, scalable and memory-efficient method for static decoding graph construction is presented. As an alternative to the traditional transducer-based approach, it is based on incremental composition. Memory efficiency is achieved by combining composition, determinization and minimization into a single step, thus eliminating large intermediate graphs. We have previously reported the use of inc...

Conventional hidden Markov models (HMMs) have weak duration constraints. This may cause the decoder to produce word matches with unrealistic durations in noisy situations. This paper describes techniques for modelling context-dependent word duration cues and incorporating them directly in a multi-stack decoding algorithm. The proposed model is capable of penalising duration constraints of a wor...

In this paper we study generalized Reed-Solomon codes (GRS codes) over commutative, noncommutative rings, show that the classical Welch-Berlekamp and Guruswami-Sudan decoding algorithms still hold in this context and we investigate their complexities. Under some hypothesis, the study of noncommutative generalized Reed-Solomon codes over finite rings leads to the fact that GRS code over commutat...

Context-based adaptive variable length coding (CAVLC) is a new and efficient entropy coding tool in H.264/AVC (advanced video coding). This study proposes a low-power and cost-effective CAVLC decoding architecture for the H.264/ AVC baseline profile. Specifically, this study proposes an optimum two-layer power model for the variable length look-up table (VLUT) in the CAVLC decoder, and divides ...

Aiming to solve the problem of high memory access and long table look-up time in table look-up of CAVLC (Context-based Adaptive Variable Length Coding ) for H.264/ AVC , a efficient look-up algorithm based on program code is presented in table look-up for CAVLC decoding in this paper, based on the analysis of the structure of CAVLC code table .The basic idea of this algorithm is that a method b...