Score normalization using phoneme-based entropy for spoken term detection

This study investigates and demonstrates the effectiveness of utilizing the entropy of a query term in spoken term detection (STD) for score normalization. It is important to normalize scores of detected terms because the optimal threshold for the decision process of detected candidates is commonly set for all query terms. A query term with higher phoneme-based entropy rather than the average entropy value of a query set is probably difficult to correctly recognize using automatic speech recognition. Thus, it cannot be detected with high accuracy if the same threshold is set for all query terms. Therefore, we propose a score normalization method in which a calibrated matching score between a query term and an index made of target spoken documents is dynamically calculated using phoneme-based entropy of the query term on a dynamic time warping-based STD framework. We evaluated this framework with query entropy on an STD task. The result indicated that it worked quite well and significantly improved STD performance compared with the baseline STD system with a pooling-based evaluation framework.