Spoken Term Detection Using SVM-Based Classifier Trained with Pre-Indexed Keywords

Abstract

SUMMARYThis study presents a two-stage spoken term detection (STD) method that uses the same STD engine twice and a support vector machine (SVM)-based classifier to verify detected terms from the STD engine's output. In a front-end process, the STD engine is used to preindex target spoken documents from a keyword list built from an automatic speech recognition result. The STD result includes a set of keywords and their detection intervals (positions) in the spoken documents. For keywords having competitive intervals, we rank them based on the STD matching cost and select the one having the longest duration among competitive detections. The selected keywords are registered in the pre-index. They are then used to train an SVM-based classifier. In a query term search process, a query term is searched by the same STD engine, and the output candidates are verified by the SVM-based classifier. Our proposed twostage STD method with pre-indexing was evaluated using the NTCIR-10 SpokenDoc-2 STD task and it drastically outperformed the traditional STD method based on dynamic time warping and a confusion network-based index.

An Empirical Study of TextRank for Keyword Extraction

“…[4] extracts candidate features from unstructured texts based on generating a sentence parse tree for each sentence in the input text. [28] presents a two-stage spoken term detection (STD) method based on support vector machine (SVM). [29] uses Maximum Entropy Partitioning (MEP) to obtain the top partition of distinctively high occurring keywords in each class.…”