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Featherweight phonetic keyword search for conversational speech

机译:羽量子拼音关键字搜索对会话语音

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The point process model (PPM) for keyword search is a phonetic event-driven approach that provides a whole-word focused alternative to fast lattice matching techniques. Recent efforts in PPMs have been focused on improved model estimation techniques and efficient search algorithms, but past evaluations have been limited to searching relatively easy scripted corpora for simple unigram queries, preventing comprehensive benchmarking against standard search methods. In this paper, we present techniques for score normalization and the processing of multi-word and out of training query terms as required by the 2006 NIST Spoken Term Detection (STD) evaluation, permitting the first comprehensive benchmark of PPM search technology against state-of-the-art word and phonetic-based search systems. We demonstrate PPM to be the fastest phonetic system while posting accuracies competitive with the best phonetic alternatives. Moreover, index construction time and size are better than any keyword search system entered in the NIST evaluation.
机译:关键字搜索的点流程模型(PPM)是一种语音事件驱动方法,提供了一个全字聚焦的替代方案到快速格子匹配技术。 PPMS中最近的努力一直专注于改进的模型估计技术和高效的搜索算法,但过去的评估仅限于搜索相对容易的脚本语料库,以防止对标准搜索方法的全面基准测试。在本文中,我们目前根据2006 NIST说明的术语检测(STD)评估要求的评分标准化和处理多字和培训查询术语的处理,允许PPM搜索技术的第一个全面基准反对状态 - 艺术基于词和语音的搜索系统。我们向PPM展示了最快的语音系统,同时发布了最佳语音替代品竞争的准确性。此外,索引施工时间和大小优于在NIST评估中输入的任何关键字搜索系统。

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