@inproceedings{ab8e0689a777418aa4f53bb3fd8ceb80,
title = "A refined query-by-example approach to spoken-term-detection on ESL learners' speech",
abstract = "A refined Query-by-Example (QbE) approach is proposed to improve Spoken-Term-Detection (STD) performance on L2 English learners' speech data. A Hidden Markov Model (HMM) is built for each keyword and a computationally efficient, iterative Viterbi decoding is adopted to detect spoken keywords in test. The approach is evaluated on an English as Second Language (ESL) speech database collected over L2 learners with different English proficiency levels. The experimental results show that the new approach achieves a performance better than the traditional DTW-based QbE. Also, it is comparable to that of an LVCSR-based STD but with significant lower complexities and computations. The refined QbE and LVCSR approach to STD are complementary to each other. By fusing the two systems together, we can further improve the MAP and MP@N performance by 6.1%-13.4% and 7.5%-14.4%, respectively, in testing sets of 3 different English proficiency levels over the best performance of either system.",
keywords = "CALL, HMM, QbE, STD, Viterbi",
author = "Jingyong Hou and Wenping Hu and Soong, {Frank K.} and Lei Xie",
note = "Publisher Copyright: � 2018 IEEE; 11th International Symposium on Chinese Spoken Language Processing, ISCSLP 2018 ; Conference date: 26-11-2018 Through 29-11-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ISCSLP.2018.8706705",
language = "英语",
series = "2018 11th International Symposium on Chinese Spoken Language Processing, ISCSLP 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "111--115",
booktitle = "2018 11th International Symposium on Chinese Spoken Language Processing, ISCSLP 2018 - Proceedings",
}