The reliability-based heuristic search methods for maximum likelihood decoding (MLD) generate test error patterns (or, equivalently, candidate codewords) according to their heuristic values. Test error patterns are stored in lists and its space complexity is crucially large for MLD of long block codes. Based on the decoding algorithms both of Battail and Fang and of its generalized version suggested by Valembois and Fossorier, we propose a new method for reducing the space complexity of the heuristic search methods for MLD including the well-known decoding algorithm of Han et al. If the heuristic function satisfies a certain condition, the proposed method guarantees to reduce the space complexity of both the Battail-Fang and Han et al. decoding algorithms. Simulation results show the high efficiency of the proposed method.

In the last three decades of the 20th Century, research in speech recognition has been intensively carried out worldwide, spurred on by advances in signal processing, algorithms, architectures, and hardware. Recognition systems have been developed for a wide variety of applications, ranging from small vocabulary keyword recognition over dial-up telephone lines, to medium size vocabulary voice interactive command and control systems for business automation, to large vocabulary speech dictation, spontaneous speech understanding, and limited-domain speech translation. Although we have witnessed many new technological promises, we have also encountered a number of practical limitations that hinder a widespread deployment of applications and services. On one hand, fast progress was observed in statistical speech and language modeling. On the other hand only spotty successes have been reported in applying knowledge sources in acoustics, speech and language science to improving speech recognition performance and robustness to adverse conditions. In this paper we review some key advances in several areas of speech recognition. A bottom-up detection framework is also proposed to facilitate worldwide research collaboration for incorporating technology advances in both statistical modeling and knowledge integration into going beyond the current speech recognition limitations and benefiting the society in the 21st century.

This paper proposes new real–time heuristic distributed parallel algorithms for search, which are based on the concepts of propagations and competitions of concurrent waves. These algorithms are characterized by simplicity and clearness of control strategies for search, and distinguished abilities in many aspects, such as real–time performance, wide suitability for searching AND/OR implicit graphs, and ease in hardware implementation.