STD based on Hough Transform and SDR using STD results: Experiments at NTCIR-9 SpokenDoc.
Taisuke Kaneko, Tomoko Takigami, Tomoyosi Akiba · NTCIR · 2011
In this paper, we report our experiments at NTCIR-9 IR for Spoken Documents (SpokenDoc) task. We participated both the STD and SDR subtasks of SpokenDoc. For STD subtask, we applied novel indexing method, called metric subspace indexing, previously proposed by us. One of the distinctive advantages of the method was that it could output the detection results in increasing order of distance without using any predefined threshold for the distance. The experimental results showed that the proposed method was very fast but there were rooms for improvement in the detection accuracy. For SDR subtask, two kinds of approaches were applied to both the lecture and passage level. The first approach used the conventional word-based IR methods based on the language modeling IR models. The second approach used the STD method for detecting the terms in the query from the spoken documents and then applied the IR methods using the detection as the term’s appearances. The experimental results showed that, though the performance of the STD-based method was lower than the word-based approaches in total, it could improve the performance if the query topic included the out-of-vocabulary words.