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A real-time hardware-based large vocabulary speech recognizer requires high memory bandwidth. We have developed a field-programmable-gate-array (FPGA)-based 20 000-word speech recognizer utilizing efficient dynamic random access memory (DRAM) access. This system contains all the functional blocks for hidden-Markov-model-based speaker-independent continuous speech recognition: feature extraction, emission probability computation, and intraword and interword Viterbi beam search. The feature extraction is conducted in software on a soft-core-based CPU, while the other functional units are implemented using parallel and pipelined hardware blocks. In order to reduce the number of memory access operations, we used several techniques such as bitwidth reduction of the Gaussian parameters, multiframe computation of the emission probability, and two-stage language model pruning. We also employ a customized DRAM controller that supports various access patterns optimized for each functional unit of the speech recognizer. The speech recognition hardware was synthesized for the Virtex-4 FPGA, and it operates at 100 MHz. The experimental result on Nov 92 20 k test set shows that the developed system runs 1.52 and 1.39 times faster than real time using the bigram and trigram language models, respectively.