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Dynamic programming search techniques for across-word modelling in speech recognition

机译:动态编程搜索技术在语音识别中的跨文体建模

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We describe the integration of across-word models in the RWTH large vocabulary continuous speech recognition system, where our main focus is on the realization of the acoustic recognition process. This paper presents a study of two search methods based on the principle of dynamic programming. For both methods we discuss the implementation details and give experimental results on the Verbmobil and on the Wall Street Journal data. In addition, we introduce a score interpolation of within-word and across-word models for both search methods. In combination with across-word models this interpolation technique gives an improvement of the recognition accuracy by 14% relative to our standard system.
机译:我们描述了在RWTH大型词汇连续语音识别系统中的跨文体模型的集成,其中我们的主要重点是实现声学识别过程的实现。 本文介绍了一种基于动态规划原理的两种搜索方法的研究。 对于这两种方法,我们讨论了实施细节,并在蜘蛛网中和华尔街日报数据上给出实验结果。 此外,我们还介绍了单词内和跨文模型的分数插值,用于两个搜索方法。 结合跨文模型,这种插值技术相对于我们的标准系统将识别准确性提高14%。

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