Spoken Term Detection (STD) is the process of locating the occurrences of spoken queries in a given speech database. Generally, two methods are adopted for STD: an ASR based sequence matching and ASR-free, feature-based template matching. If a well-performing ASR is available, the former STD method is accurate. However, to build an ASR with consistent performance, several hours of labelled corpora is required. Template matching methods work well for small or chopped utterances. However, in practice, the volume of the search database can be huge, containing sentences of varying lengths. Hence time complexity of template matching techniques will be high, which makes them impractical for realistic search applications. In this work, a two-stage STD system is proposed, which combines the ASR-based phoneme sequence matching in the first stage and feature sequence template matching of selected locations in the second stage. The time complexity of the second stage is reduced by performing DTW-based template matching only at probable query locations identified by the first stage. ‘Split and match’ approach helps to reduce the false-positives in case of longer query words. Effectiveness of the proposed method is demonstrated using English and Malayalam datasets.

中文翻译：

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资源不足语言的两阶段口语检测系统

语音术语检测（STD）是在给定语音数据库中定位语音查询发生的过程。通常，STD采用两种方法：基于ASR的序列匹配和基于ASR的无特征模板匹配。如果有性能良好的ASR，则前一种STD方法是准确的。但是，要构建具有一致性能的ASR，需要几个小时的标记语料库。模板匹配方法适用于较小或切碎的话语。但是，实际上，搜索数据库的容量可能很大，其中包含长度可变的句子。因此，模板匹配技术的时间复杂度将很高，这使得它们对于现实的搜索应用不切实际。在这项工作中，提出了两阶段性病系统，它在第一阶段结合了基于ASR的音素序列匹配，在第二阶段结合了所选位置的特征序列模板匹配。通过仅在第一阶段标识的可能查询位置执行基于DTW的模板匹配，可以减少第二阶段的时间复杂度。“拆分和匹配”方法有助于减少查询词较长时的假阳性。使用英语和马拉雅拉姆语数据集证明了该方法的有效性。