Entropy-based false detection filtering in spoken term detection tasks
Satoshi Natori, Yuto Furuya, Hiromitsu Nishizaki, Yoshihiro Sekiguchi · 2013
This paper describes spoken term detection (STD) and inexistent STD (iSTD) methods using term detection entropy based on a phoneme transition network (PTN)-formed index. Our previously reported STD method uses a PTN derived from multiple automatic speech recognizers (ASRs) as an index. A PTN is almost the same as a sub-word-based confusion network, which is derived from the output of an ASR. In the previous study, our PTN was very effective in detecting query terms. However, the PTN generated many false detection errors. In this study, we focus on entropy of the PTN-formed index. Entropy is used to filter out false detection candidates in the second pass of the STD process. Our proposed method was evaluated using the Japanese standard test-set for the STD and iSTD tasks. The experimental results of the STD task showed that entropy-based filtering is effective for improving STD at a high-recall range. In addition, entropy-based filtering was also demonstrated to work well for the iSTD task.