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Large Vocabulary Speaker Independent Isolated Word Recognition for Embedded Systems

机译:嵌入式系统中的大词汇量扬声器独立的独立单词识别

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摘要

In this paper the implementation of a word-stem based tree search for large vocabulary speaker independent isolated word recognition for embedded systems is presented. Two fast search algorithms combine the effectiveness of the tree structure for large vocabularies and the fast Viterbi search within the regular structures of word-stems. The algorithms are proved to be very effective for workstation and embedded platform realizations. In order to decrease the processing power the word-stem based tree search with frame dropping approach is used. The recognition speed was increased by a factor of 5 without frame dropping and by a factor of 10 with frame dropping in comparison to linear Viterbi search for isolated word recognition task with a vocabulary of 20102 words. Thus, the large vocabulary isolated word recognition becomes possible for embedded systems.
机译:本文提出了一种基于词干树的搜索系统,用于嵌入式系统的大词汇量说话者独立的孤立词识别。两种快速搜索算法将大词汇量的树状结构的有效性与单词词干的常规结构内的快速维特比搜索相结合。实践证明,该算法对于工作站和嵌入式平台的实现非常有效。为了降低处理能力,使用了基于词干的具有丢帧方法的树搜索。相对于线性维特比搜索(含单词为20102个单词的孤立单词识别任务),识别速度增加了5倍(不丢帧),增加了10倍(不丢帧)。因此,对于嵌入式系统而言,大词汇量隔离单词识别成为可能。

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