Toward High-Performance Language-Independent Query-by-Example Spoken Term Detection for MediaEval 2015: Post-Evaluation Analysis

Toward High-Performance Language-Independent Query-by-Example Spoken Term Detection for MediaEval 2015: Post-Evaluation Analysis

INTERSPEECH 2016

10.21437/Interspeech.2016-691

Cheung-Chi Leung, Lei Wang, Haihua Xu, Jingyong Hou, Tung Pham Van, Hang Lv, Lei Xie, Xiong Xiao, Chongjia Ni, Bin Ma, Eng Siong Chng, Haizhou Li

Data Augmentation, bottleneck features, dynamic time warping, symbolic search, partial matching

08 September 2016

Leung, C., Wang, L., Xu, H., Hou, J., Pham, V.T., Lv, H., Xie, L., Xiao, X., Ni, C., Ma, B., Chng, E.S., Li, H. (2016) Toward High-Performance Language-Independent Query-by-Example Spoken Term Detection for MediaEval 2015: Post-Evaluation Analysis. Proc. Interspeech 2016, 3703-3707.

This paper documents the significant components of a state-of-the-art language-independent query-by-example spoken term detection system designed for the Query by Example Search on Speech Task (QUESST) in MediaEval 2015. We developed exact and partial matching DTW systems, and WFST based symbolic search systems to handle different types of search queries. To handle the noisy and reverberant speech in the task, we trained tokenizers using data augmented with different noise and reverberation conditions. Our post-evaluation analysis showed that the phone boundary label provided by the improved tokenizers brings more accurate speech activity detection in DTW systems. We argue that acoustic condition mismatch is possibly a more important factor than language mismatch for obtaining consistent gain from stacked bottleneck features. Our post-evaluation system, involving a smaller number of component systems, can outperform our submitted systems, which performed the best for the task.

PublisherCopyrights

https://oar.a-star.edu.sg/communities-collections/articles/14483

Institute for Infocomm Research