N-Best Breadth Search For Large Vocabulary Continuous Speech Recognition Using A Long Span Language Model (1998)
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BibTeX
@MISC{Yuk98n-bestbreadth,
author = {Dongsuk Yuk and Chiwei Che and Prabhu Raghavan and James Flanagan},
title = {N-Best Breadth Search For Large Vocabulary Continuous Speech Recognition Using A Long Span Language Model},
year = {1998}
}
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Abstract
In large vocabulary continuous speech recognition, high level linguistic knowledge can enhance performance. However, integration of high level linguistic knowledge and complex acoustic models under an efficient search scheme is still an open question. In this paper, we propose the n-best breadth search algorithm under the framework of a state space search. The n-best breadth search is a combination of the best first search and the breadth first search, and it efficiently accommodates the long span language models and complex acoustic models. Our pilot experiment shows that the proposed algorithm decreases execution time with little effect on performance. 136th Meeting of Acoustical Society of America 2 Contents 1 INTRODUCTION 3 2 REVIEW OF DECODING ALGORITHMS 4 3 N-BEST BREADTH SEARCH 5 4 IMPLEMENTATION ISSUES 7 5 EXPERIMENTAL RESULTS 8 6 CONCLUSIONS 9 7 ACKNOWLEDGMENT 136th Meeting of Acoustical Society of America 3 1 INTRODUCTION In the statistical approach, speech recognition ...
