Language independent query-by-example spoken term detection using N-best phone sequences and partial matching
Haihua Xu, Peng Yang, Xiong Xiao, Lei Xie, Cheung-Chi Leung, Hongjie Chen, Jia Yu, Hang Lv
Abstract
In this paper, we propose a partial sequence matching based symbolic search (SS) method for the task of language independent query-by-example spoken term detection. One main drawback of conventional SS approach is the high miss rate for long queries. This is due to high variations in symbol representation of query and search audios, especially in language independent scenario. The successful matching of a query with its instances in search audio becomes exponentially more difficult as the query grows longer. To reduce miss rate, we propose a partial matching strategy, in which all partial phone sequences of a query are used to search for query instances. The partial matching is also suitable for real life applications where exact match is usually not necessary and word prefix, suffix, and order should not affect the search result. When applied to the QUESST 2014 task, results show the partial matching of phone sequences is able to reduce miss rate of long queries significantly compared with conventional full matching method. In addition, for the most challenging inexact matching queries (type 3), it also shows clear advantage over DTW-based methods.
BibTeX
@inproceedings{icassp2015_languageindepend,
title = {Language independent query-by-example spoken term detection using N-best phone sequences and partial matching},
author = {Haihua Xu and Peng Yang and Xiong Xiao and Lei Xie and Cheung-Chi Leung and Hongjie Chen and Jia Yu and Hang Lv and Lei Wang and Su Jun Leow and Bin Ma and Engsiong Chng and Haizhou Li},
booktitle = {ICASSP 2015},
year = {2015}
}