Spoken Term Detection Using Spoken Document Index Based on Keywords Collected from Automatic Speech Recognition Result

Abstract

This paper presents a novel spoken document indexing framework for Spoken Term Detection (STD). Our proposed method utilizes an STD method for making an index from keywords collected from outputs from automatic speech recognition systems. The STD method is conducted for all the keywords as query terms; then, the detection result, a set of each keyword and its detection intervals in the spoken document, is obtained. For the keywords that have competitive intervals, we rank them based on the matching cost of STD and select the best one with the longest duration among competitive detections. This is the final output of STD process and serves as an index word for the spoken document. The proposed framework was evaluated on real lecture speeches as spoken documents in an STD task. The results show that our framework was quite effective for preventing false detection errors and in annotating keyword indices to spoken documents.