Novel methods for query selection and query combination in query-by-example spoken term detection

SSCS '10 Pub Date : 2010-10-29 DOI:10.1145/1878101.1878106
Javier Tejedor, Igor Szöke, M. Fapšo
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引用次数: 21

Abstract

Query-by-example (QbE) spoken term detection (STD) is necessary for low-resource scenarios where training material is hardly available and word-based speech recognition systems cannot be employed. We present two novel contributions to QbE STD: the first introduces several criteria to select the optimal example used as query throughout the search system. The second presents a novel feature level example combination to construct a more robust query used during the search. Experiments, tested on with-in language and cross-lingual QbE STD setups, show a significant improvement when the query is selected according to an optimal criterion over when the query is selected randomly for both setups and a significant improvement when several examples are combined to build the input query for the search system compared with the use of the single best example. They also show comparable performance to that of a state-of-the-art acoustic keyword spotting system.
基于实例查询的语音词检测中查询选择和查询组合的新方法
基于示例的查询(Query-by-example, QbE)语音术语检测(STD)对于资源匮乏的场景是必要的,在这些场景中,训练材料很难获得,并且无法使用基于单词的语音识别系统。我们对QbE STD提出了两个新的贡献:第一个引入了几个标准来选择在整个搜索系统中用作查询的最优示例。第二部分提出了一种新的特征级示例组合,以构建在搜索过程中使用的更健壮的查询。在语言内和跨语言的QbE STD设置上测试的实验表明,当根据最优标准选择查询时,与在两种设置中随机选择查询时相比,在根据最优标准选择查询时有显着改善,并且当将多个示例组合起来构建搜索系统的输入查询时,与使用单个最佳示例相比有显着改善。它们还显示出与最先进的声学关键字定位系统相当的性能。
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