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Speech Retrieval (SLP-IR)

An LSTM-CTC based verification system for proxy-word based OOV keyword search

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Authors:
Zhiqiang Lv, Jian Kang, Wei-Qiang Zhang, Jia Liu
Submitted On:
7 March 2017 - 4:30am
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An LSTM-CTC based verification system for proxy-word based OOV keyword search.pptx

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[1] Zhiqiang Lv, Jian Kang, Wei-Qiang Zhang, Jia Liu, "An LSTM-CTC based verification system for proxy-word based OOV keyword search", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1683. Accessed: Jun. 23, 2017.
@article{1683-17,
url = {http://sigport.org/1683},
author = {Zhiqiang Lv; Jian Kang; Wei-Qiang Zhang; Jia Liu },
publisher = {IEEE SigPort},
title = {An LSTM-CTC based verification system for proxy-word based OOV keyword search},
year = {2017} }
TY - EJOUR
T1 - An LSTM-CTC based verification system for proxy-word based OOV keyword search
AU - Zhiqiang Lv; Jian Kang; Wei-Qiang Zhang; Jia Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1683
ER -
Zhiqiang Lv, Jian Kang, Wei-Qiang Zhang, Jia Liu. (2017). An LSTM-CTC based verification system for proxy-word based OOV keyword search. IEEE SigPort. http://sigport.org/1683
Zhiqiang Lv, Jian Kang, Wei-Qiang Zhang, Jia Liu, 2017. An LSTM-CTC based verification system for proxy-word based OOV keyword search. Available at: http://sigport.org/1683.
Zhiqiang Lv, Jian Kang, Wei-Qiang Zhang, Jia Liu. (2017). "An LSTM-CTC based verification system for proxy-word based OOV keyword search." Web.
1. Zhiqiang Lv, Jian Kang, Wei-Qiang Zhang, Jia Liu. An LSTM-CTC based verification system for proxy-word based OOV keyword search [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1683

An LSTM-CTC based verification system for proxy-word based OOV keyword search

Paper Details

Authors:
Zhiqiang Lv, Jian Kang, Wei-Qiang Zhang, Jia Liu
Submitted On:
7 March 2017 - 4:30am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

An LSTM-CTC based verification system for proxy-word based OOV keyword search.pptx

(32 downloads)

Keywords

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[1] Zhiqiang Lv, Jian Kang, Wei-Qiang Zhang, Jia Liu, "An LSTM-CTC based verification system for proxy-word based OOV keyword search", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1681. Accessed: Jun. 23, 2017.
@article{1681-17,
url = {http://sigport.org/1681},
author = {Zhiqiang Lv; Jian Kang; Wei-Qiang Zhang; Jia Liu },
publisher = {IEEE SigPort},
title = {An LSTM-CTC based verification system for proxy-word based OOV keyword search},
year = {2017} }
TY - EJOUR
T1 - An LSTM-CTC based verification system for proxy-word based OOV keyword search
AU - Zhiqiang Lv; Jian Kang; Wei-Qiang Zhang; Jia Liu
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1681
ER -
Zhiqiang Lv, Jian Kang, Wei-Qiang Zhang, Jia Liu. (2017). An LSTM-CTC based verification system for proxy-word based OOV keyword search. IEEE SigPort. http://sigport.org/1681
Zhiqiang Lv, Jian Kang, Wei-Qiang Zhang, Jia Liu, 2017. An LSTM-CTC based verification system for proxy-word based OOV keyword search. Available at: http://sigport.org/1681.
Zhiqiang Lv, Jian Kang, Wei-Qiang Zhang, Jia Liu. (2017). "An LSTM-CTC based verification system for proxy-word based OOV keyword search." Web.
1. Zhiqiang Lv, Jian Kang, Wei-Qiang Zhang, Jia Liu. An LSTM-CTC based verification system for proxy-word based OOV keyword search [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1681

Pairwise Learning using Multi-lingual Bottleneck Features for Low-resource Query-by-example Spoken Term Detection


We propose to use a feature representation obtained by pairwise learning in a low-resource language for query-by-example spoken term detection (QbE-STD). We assume that word pairs identified by humans are available in the low-resource target language. The word pairs are parameterized by a multi-lingual bottleneck feature (BNF) extractor that is trained using transcribed data in high-resource languages. The multi-lingual BNFs of the word pairs are used as an initial feature representation to train an autoencoder (AE).

Paper Details

Authors:
Yougen Yuan, Cheung-Chi Leung, Lei Xie, Hongjie Chen, Bin Ma, Haizhou Li
Submitted On:
7 March 2017 - 11:01pm
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ICASSP2017_oral_ygyuan.pdf

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[1] Yougen Yuan, Cheung-Chi Leung, Lei Xie, Hongjie Chen, Bin Ma, Haizhou Li, "Pairwise Learning using Multi-lingual Bottleneck Features for Low-resource Query-by-example Spoken Term Detection", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1598. Accessed: Jun. 23, 2017.
@article{1598-17,
url = {http://sigport.org/1598},
author = {Yougen Yuan; Cheung-Chi Leung; Lei Xie; Hongjie Chen; Bin Ma; Haizhou Li },
publisher = {IEEE SigPort},
title = {Pairwise Learning using Multi-lingual Bottleneck Features for Low-resource Query-by-example Spoken Term Detection},
year = {2017} }
TY - EJOUR
T1 - Pairwise Learning using Multi-lingual Bottleneck Features for Low-resource Query-by-example Spoken Term Detection
AU - Yougen Yuan; Cheung-Chi Leung; Lei Xie; Hongjie Chen; Bin Ma; Haizhou Li
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1598
ER -
Yougen Yuan, Cheung-Chi Leung, Lei Xie, Hongjie Chen, Bin Ma, Haizhou Li. (2017). Pairwise Learning using Multi-lingual Bottleneck Features for Low-resource Query-by-example Spoken Term Detection. IEEE SigPort. http://sigport.org/1598
Yougen Yuan, Cheung-Chi Leung, Lei Xie, Hongjie Chen, Bin Ma, Haizhou Li, 2017. Pairwise Learning using Multi-lingual Bottleneck Features for Low-resource Query-by-example Spoken Term Detection. Available at: http://sigport.org/1598.
Yougen Yuan, Cheung-Chi Leung, Lei Xie, Hongjie Chen, Bin Ma, Haizhou Li. (2017). "Pairwise Learning using Multi-lingual Bottleneck Features for Low-resource Query-by-example Spoken Term Detection." Web.
1. Yougen Yuan, Cheung-Chi Leung, Lei Xie, Hongjie Chen, Bin Ma, Haizhou Li. Pairwise Learning using Multi-lingual Bottleneck Features for Low-resource Query-by-example Spoken Term Detection [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1598

Exploiting noisy web data by OOV ranking for low-resource keyword search


Spoken keyword search in low-resource condition suffers from out-of-vocabulary (OOV) problem and insufficient text data for language model (LM) training. Web-crawled text data is used to expand vocabulary and to augment language model. However, the mismatching between web text and the target speech data brings difficulties to effective utilization. New words from web data need an evaluation to exclude noisy words or introduce proper probabilities. In this paper, several criteria to rank new words from web data are investigated and are used as features

Paper Details

Authors:
Ji Wu
Submitted On:
15 October 2016 - 7:55am
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ISCSLP2016_Poster_Exploiting.pdf

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[1] Ji Wu, "Exploiting noisy web data by OOV ranking for low-resource keyword search", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1232. Accessed: Jun. 23, 2017.
@article{1232-16,
url = {http://sigport.org/1232},
author = {Ji Wu },
publisher = {IEEE SigPort},
title = {Exploiting noisy web data by OOV ranking for low-resource keyword search},
year = {2016} }
TY - EJOUR
T1 - Exploiting noisy web data by OOV ranking for low-resource keyword search
AU - Ji Wu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1232
ER -
Ji Wu. (2016). Exploiting noisy web data by OOV ranking for low-resource keyword search. IEEE SigPort. http://sigport.org/1232
Ji Wu, 2016. Exploiting noisy web data by OOV ranking for low-resource keyword search. Available at: http://sigport.org/1232.
Ji Wu. (2016). "Exploiting noisy web data by OOV ranking for low-resource keyword search." Web.
1. Ji Wu. Exploiting noisy web data by OOV ranking for low-resource keyword search [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1232

KEYWORD SEARCH USING QUERY EXPANSION FOR GRAPH-BASED RESCORING OF HYPOTHESIZED DETECTIONS


In this work, we propose a novel framework for rescoring keyword search (KWS) detections using acoustic samples extracted from the training data. We view the keyword rescoring task as an information retrieval task and adopt the idea of query expansion. We expand a textual keyword with multiple speech keyword samples extracted from the training data. In this way, the hypothesized detections are compared with the multiple keywords using non-parametric approaches such as dynamic time warping (DTW).

Paper Details

Authors:
Van Tung Pham, Haihua Xu, Xiong Xiao, Nancy F. Chen, Eng Siong Chng, Haizhou Li
Submitted On:
22 March 2016 - 10:16am
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Document Files

icassp2016_with_note_v3.pdf

(136 downloads)

Keywords

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[1] Van Tung Pham, Haihua Xu, Xiong Xiao, Nancy F. Chen, Eng Siong Chng, Haizhou Li, "KEYWORD SEARCH USING QUERY EXPANSION FOR GRAPH-BASED RESCORING OF HYPOTHESIZED DETECTIONS", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/970. Accessed: Jun. 23, 2017.
@article{970-16,
url = {http://sigport.org/970},
author = {Van Tung Pham; Haihua Xu; Xiong Xiao; Nancy F. Chen; Eng Siong Chng; Haizhou Li },
publisher = {IEEE SigPort},
title = {KEYWORD SEARCH USING QUERY EXPANSION FOR GRAPH-BASED RESCORING OF HYPOTHESIZED DETECTIONS},
year = {2016} }
TY - EJOUR
T1 - KEYWORD SEARCH USING QUERY EXPANSION FOR GRAPH-BASED RESCORING OF HYPOTHESIZED DETECTIONS
AU - Van Tung Pham; Haihua Xu; Xiong Xiao; Nancy F. Chen; Eng Siong Chng; Haizhou Li
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/970
ER -
Van Tung Pham, Haihua Xu, Xiong Xiao, Nancy F. Chen, Eng Siong Chng, Haizhou Li. (2016). KEYWORD SEARCH USING QUERY EXPANSION FOR GRAPH-BASED RESCORING OF HYPOTHESIZED DETECTIONS. IEEE SigPort. http://sigport.org/970
Van Tung Pham, Haihua Xu, Xiong Xiao, Nancy F. Chen, Eng Siong Chng, Haizhou Li, 2016. KEYWORD SEARCH USING QUERY EXPANSION FOR GRAPH-BASED RESCORING OF HYPOTHESIZED DETECTIONS. Available at: http://sigport.org/970.
Van Tung Pham, Haihua Xu, Xiong Xiao, Nancy F. Chen, Eng Siong Chng, Haizhou Li. (2016). "KEYWORD SEARCH USING QUERY EXPANSION FOR GRAPH-BASED RESCORING OF HYPOTHESIZED DETECTIONS." Web.
1. Van Tung Pham, Haihua Xu, Xiong Xiao, Nancy F. Chen, Eng Siong Chng, Haizhou Li. KEYWORD SEARCH USING QUERY EXPANSION FOR GRAPH-BASED RESCORING OF HYPOTHESIZED DETECTIONS [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/970