One-pass Decoding Research Articles

Joint message passing decoding for LDPC codes with CCDMs.

In this Letter, message passing algorithms are introduced to jointly decode low-density parity-check (LDPC) codes and constant composition distribution matchers (CCDMs) to achieve better performance. Then, based on the weight constraint of CCDMs, a weight decoding with linear complexity is proposed to efficiently decode the CCDM codewords. Simulations over a fiber channel show that short CCDMs with the proposed joint decoding provide up to 10.6% reach increase compared with long CCDMs with one-pass decoding.

An Efficient Eligible Error Locator Polynomial Searching Algorithm and Hardware Architecture for One-Pass Chase Decoding of BCH Codes

In numerous memory and communication systems, Bose–Chaudhuri–Hocquenghem (BCH) codes are widely employed to enhance reliability. A one-pass Chase soft-decision decoding algorithm for BCH codes was previously proposed to achieve significant performance improvement over traditional hard-decision decoding while not increasing too much computational complexity. The bottleneck in conventional one-pass Chase decoding is the procedure of judging whether an obtained error locator polynomial is valid. In this brief, a novel algorithm that can efficiently verify eligibility of each generated error locator polynomial is proposed. The problem is first reformulated as a polynomial modulo problem, where repeated squaring can be employed for further simplification. In order to decrease the critical path delay and hardware complexity, an efficient polynomial division algorithm based on polynomial inversion is also proposed. In addition, a VLSI architecture for the proposed algorithm is presented. The implemented results show that the proposed eligibility checking algorithm reduces the gate counts to only 12% of a conventional polynomial selection algorithm without introducing any speed penalty. The projected area reduction achieved in a complete one-pass Chase decoder is approximately 75%. In addition, post-layout simulation shows that the proposed algorithm is 20 times more power efficient than the conventional method.

An information fusion framework with multi-channel feature concatenation and multi-perspective system combination for the deep-learning-based robust recognition of microphone array speech

We present an information fusion approach to the robust recognition of multi-microphone speech. It is based on a deep learning framework with a large deep neural network (DNN) consisting of subnets designed from different perspectives. Multiple knowledge sources are then reasonably integrated via an early fusion of normalized noisy features with multiple beamforming techniques, enhanced speech features, speaker-related features, and other auxiliary features concatenated as the input to each subnet to compensate for imperfect front-end processing. Furthermore, a late fusion strategy is utilized to leverage the complementary natures of the different subnets by combining the outputs of all subnets to produce a single output set. Testing on the CHiME-3 task of recognizing microphone array speech, we demonstrate in our empirical study that the different information sources complement each other and that both early and late fusions provide significant performance gains, with an overall word error rate of 10.55% when combining 12 systems. Furthermore, by utilizing an improved technique for beamforming and a powerful recurrent neural network (RNN)-based language model for rescoring, a WER of 9.08% can be achieved for the best single DNN system with one-pass decoding among all of the systems submitted to the CHiME-3 challenge.

Low-Complexity Multiresolution Image Compression Using Wavelet Lower Trees

In this paper, a new image compression algorithm is proposed based on the efficient construction of wavelet coefficient lower trees. The main contribution of the proposed lower-tree wavelet (LTW) encoder is the utilization of coefficient trees, not only as an efficient method of grouping coefficients, but also as a fast way of coding them. Thus, it presents state-of-the-art compression performance, whereas its complexity is lower than the one presented in other wavelet coders, like SPIHT and JPEG 2000. Fast execution is achieved by means of a simple two-pass coding and one-pass decoding algorithm. Moreover, its computation does not require additional lists or complex data structures, so there is no memory overhead. A formal description of the algorithm is provided, while reference software is also given. Numerical results show that our codec works faster than SPIHT and JPEG 2000 (up to three times faster than SPIHT and fifteen times faster than JPEG 2000), with similar coding efficiency

Attribute-based word modeling

An attribute-based speech recognition system is described. A speech pre-processor receives input speech and produces a sequence of acoustic observations representative of the input speech. A database of context-dependent acoustic models characterize a probability of a given sequence of sounds producing the sequence of acoustic observations. Each acoustic model includes phonetic attributes and suprasegmental non-phonetic attributes. A finite state language model characterizes a probability of a given sequence of words being spoken. A one-pass decoder compares the sequence of acoustic observations to the acoustic models and the language model, and outputs at least one word sequence representative of the input speech.

Continuous Speech Recognition without End-point Detection

A new continuous speech recognition method that does not need the explicit speech end-point detection is proposed. A one-pass decoding algorithm is modified to decode the input speech of infinite length so that, with appropriate non-speech models for silence and ambient noises, continuous speech recognition can be executed without the explicit end-point detection. The basic algorithm 1) decodes a processing block of the predetermined length, 2) tracebacks and finds the boundaries of the processing blocks where the word history in the preceding processing block is merged into one, and 3) restarts decoding from the boundary frame with the merged word history. The effectiveness of the method is verified by the spoken dialogue transcription experiments. With a 5-minute dialogue in a moving car, the proposed method gives better results in word accuracy than the results using the explicit end-point detection method and the conventional one-pass decoder.