Combination of DTW-based and CRF-based Spoken Term Detection on the NTCIR-11 SpokenQuery&Doc SQ-STD Subtask
Hiromitsu Nishizaki, Naoki Sawada, Satoshi Natori, Kentaro Domoto, Takehito Utsuro · NTCIR · 2014
Conventional spoken term detection (STD) techniques, which use a text-based matching approach based on automatic speech recognition (ASR) systems, are not robust for speech recognition errors. This paper proposes a conditional ran- domelds (CRF)-based combination (re-ranking) approach, which recomputes detection scores produced by a phoneme- based dynamic time warping (DTW) STD approach. In the re-ranking approach, we tackle STD as a sequence label- ing problem. We use CRF-based triphone detection models based on features generated from multiple types of phoneme- based transcriptions. They train recognition error patterns such as phoneme-to-phone confusions on the CRF frame- work. Therefore, the models can detect a triphone, which is one of triphones composing a query term, with detection probability. In the experimental evaluation on the NTCIR- 11 SpokenQuery&Doc SQ-STD test collection, the CRF- based approach and the combination approach of the two STD systems could not outperform the conventional DTW- based approach we have already proposed.