In this paper, we propose a novel indexing method for Spoken Term Detection (STD). The proposed method can be considered as using metric space indexing for the approximate string-matching problem, where the distance between a phoneme and a position in the target spoken document is defined. The proposed method does not require the use of thresholds to limit the output, instead being able to output the results in increasing order of distance. It can also deal easily with the multiple candidates obtained via Automatic Speech Recognition (ASR). The results of preliminary experiments show promise for achieving fast STD.
Bibliographic reference. Kaneko, Taisuke / Akiba, Tomoyosi (2010): "Metric subspace indexing for fast spoken term detection", In INTERSPEECH-2010, 689-692.