Incorporating syllable duration into line-detection-based spoken term detection

Abstract

A conventional method for spoken term detection (STD) is to apply approximate string matching to subword sequences in a spoken document obtained by speech recognition. An STD method that considers string matching as line detection in a syllable distance plane has been proposed. While this has demonstrated fast ordered-by-distance detections, it has still suffered from the insertion and deletion errors introduced by the speech recognition. In this work, we aim to improve detection performance by employing syllable-duration information. The proposed method enables robust detection by introducing a distance plane that uses frames as units instead of using syllables as units. Our experimental evaluation showed that the incorporation of syllable-duration achieved higher detection performance in high-recall regions.