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中文摘要：
为了提高基于DTW算法的语音检索系统的速度,提出了一种基于分段累积近似下界估计的动态时间规整算法,实现语音样例快速检索.该方法首先提取查询样例和测试集的音素后验概率作为特征参数,然后计算语音样例和测试集中所有候选分段实际动态规整得分的分段累积近似下界估计,最后采用K-最近邻算法与动态时间规整算法搜索与语音样例相似度最高的区域.实验结果表明,此算法的检索速度比直接运用DTW算法快6.32倍,而对其检索精度无任何影响.
英文摘要：
In order to accelerate speed of speech retrieval system based on DTW,this paper presented a fast query-by-example spoken term detection method based on a piecewise aggregate approximation（PAA） lower-bound estimate（LBE） for dynamic time warping（DTW）.In the method,it firstly extracted the phone posterior probabilities of query examples and test materials.Then it computed the piecewise aggregate approximation lower-bound estimates between the query example and every possible matching region in the corpus of utterances.Finally,it chose the K-nearest neighbor （KNN） and DTW to search for the relevant regions.Experimental results show that the detection speed of the new method is 6.32 times as fast as applying DTW directly,and there is no effect on the detection precision when compared with the latter.
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