Abstract:Large Vocabulary Continuous Speech Recognition (LVCSR) systems require Viterbi searching through a large state space to find the most probable sequence of phonemes that led to a given sound sample. This needs storing and updating of a large Active State List (ASL) in the on-chip memory (OCM) at regular intervals (called frames), which poses a major performance bottleneck for speech decoding. Most works use hash tables for OCM storage while
beam-width pruning
to restrict the ASL size. In… Show more
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