Efficient Beam Pruning for Speech Recognition with a Reward Considering the Potential to Reach Various Words on a Lexical Tree

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This paper presents efficient frame-synchronous beam pruning for HMM-based automatic speech recognition. In the conventional beam pruning, a few hypotheses that have greater potential to reach various words on a lexical tree are likely to be pruned out by a number of hypotheses that have limited potential, since all hypotheses are treated equally without considering this potential. To make the beam pruning less restrictive for hypotheses with greater potential and vice versa, the proposed method adds to the likelihood of each hypothesis a tentative reward as a monotonically increasing function of the number of reachable words from the HMM state where the hypothesis stays in a lexical tree. The reward is designed not to collapse the ASR probabilistic framework. The proposed method reduced 84% of the processing time for a grammar-based 10k-word short sentence recognition task. For a language-model-based dictation task, it also resulted in an additional 23% reduction in processing time from the beam pruning with the language model look-ahead technique.