Conference Paper

An efficient beam pruning with a reward considering the potential to reach various words on a lexical tree

User Interface Lab., KDDI R& D Labs. Inc., Fujimino, Japan
DOI: 10.1109/ICASSP.2010.5495098 Conference: Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Source: IEEE Xplore

ABSTRACT This paper presents an efficient frame-synchronous beam pruning for automatic speech recognition. With conventional beam pruning, hypotheses that have a greater potential to reach various words on a lexical tree are likely to be pruned out, since this potential is not taken into account. To make the beam pruning less restrictive for hypotheses with a greater potential and vice versa, the proposed method adds a reward as a monotonically increasing function of the number of reachable words from the node where a hypothesis stays on a lexical tree, to the likelihood of the hypothesis. The reward is designed not to collapse the ASR probabilistic framework. The proposed method reduces the processing time from 30% to 70% for grammar-based tasks. For a language-model-based dictation task, it also causes an additional reduction from the processing time of the beam pruning with the language model look-ahead technique.

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    ABSTRACT: Abstract Improved efficiency of pruning accelerates the search process and leads to a more time efficient speech recognition system. The goal of this work,was,to develop a new,pruning tech- nique which optimizes the well known,probability-based prun- ing (beam width) by utilization of confidence measurement. We use normalized hypotheses scores to guide the beam,width of the pruning process dynamically,frame per frame during the whole,utterance. Compared,with classical pruning tech- niques like fixed beam,pruning and histogram,rank pruning we,achieved significantly better results concerning,the time consumption,of the recognizer. The speed of the recognition process could be accelerated up to 14 times with a slight degra- dation in recognition accuracy.
    INTERSPEECH 2005 - Eurospeech, 9th European Conference on Speech Communication and Technology, Lisbon, Portugal, September 4-8, 2005; 01/2005
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    The 3rd International Conference on Spoken Language Processing, ICSLP 1994, Yokohama, Japan, September 18-22, 1994; 01/1994
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    ABSTRACT: Pruning is an essential paradigm to build HMM based large vocabulary speech recognisers that use reasonable computing resources. Unlikely sentence, word or subword hypotheses are removed from the search space when their likelihood falls outside a beam relative to the best scoring hypothesis. A method for automatically steering this beam such that the search space attains a predefined size is presented
    Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on; 11/1996


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