Re-Ranking Approach of Spoken Term Detection Using Conditional Random Fields-Based Triphone Detection

  • SAWADA Naoki
    Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences, University of Yamanashi
  • NISHIZAKI Hiromitsu
    Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences, University of Yamanashi

抄録

<p>This study proposes a two-pass spoken term detection (STD) method. The first pass uses a phoneme-based dynamic time warping (DTW)-based STD, and the second pass recomputes detection scores produced by the first pass using conditional random fields (CRF)-based triphone detectors. In the second-pass, we treat STD as a sequence labeling problem. We use CRF-based triphone detection models based on features generated from multiple types of phoneme-based transcriptions. The models train recognition error patterns such as phoneme-to-phoneme confusions in the CRF framework. Consequently, the models can detect a triphone comprising a query term with a detection probability. In the experimental evaluation of two types of test collections, the CRF-based approach worked well in the re-ranking process for the DTW-based detections. CRF-based re-ranking showed 2.1% and 2.0% absolute improvements in F-measure for each of the two test collections.</p>

収録刊行物

参考文献 (16)*注記

もっと見る

関連プロジェクト

もっと見る

詳細情報

問題の指摘

ページトップへ