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In this paper we present a hardware architecture for large vocabulary continuous speech recognition that conducts a search over a weighted finite state transducer (WFST) network. A pipelined architecture is proposed to fully utilize the memory bandwidth. A hash table is used to manage small sized working sets efficiently. We also applied a parallelization technique that increases the traversal speed...
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...
We have developed a VLSI chip for 5,000 word speaker-independent continuous speech recognition. This chip employs a context-dependent HMM (hidden Markov model) based speech recognition algorithm, and contains emission probability and Viterbi beam search pipelined hardware units. The feature vector for speech recognition is computed using a host processor in software in order to adopt various enhancement...
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