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Speech Data Mining (SLP-DM)

Domain independent key term extraction from spoken content based on context and term location information in the utterances

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Authors:
Hsien-Chin Lin, Chi-Yu Yang, Hung-Yi Lee, Lin-shan Lee
Submitted On:
20 April 2018 - 1:00am
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keyterm (v4).pptx

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[1] Hsien-Chin Lin, Chi-Yu Yang, Hung-Yi Lee, Lin-shan Lee , "Domain independent key term extraction from spoken content based on context and term location information in the utterances", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2864. Accessed: Aug. 21, 2018.
@article{2864-18,
url = {http://sigport.org/2864},
author = {Hsien-Chin Lin; Chi-Yu Yang; Hung-Yi Lee; Lin-shan Lee },
publisher = {IEEE SigPort},
title = {Domain independent key term extraction from spoken content based on context and term location information in the utterances},
year = {2018} }
TY - EJOUR
T1 - Domain independent key term extraction from spoken content based on context and term location information in the utterances
AU - Hsien-Chin Lin; Chi-Yu Yang; Hung-Yi Lee; Lin-shan Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2864
ER -
Hsien-Chin Lin, Chi-Yu Yang, Hung-Yi Lee, Lin-shan Lee . (2018). Domain independent key term extraction from spoken content based on context and term location information in the utterances. IEEE SigPort. http://sigport.org/2864
Hsien-Chin Lin, Chi-Yu Yang, Hung-Yi Lee, Lin-shan Lee , 2018. Domain independent key term extraction from spoken content based on context and term location information in the utterances. Available at: http://sigport.org/2864.
Hsien-Chin Lin, Chi-Yu Yang, Hung-Yi Lee, Lin-shan Lee . (2018). "Domain independent key term extraction from spoken content based on context and term location information in the utterances." Web.
1. Hsien-Chin Lin, Chi-Yu Yang, Hung-Yi Lee, Lin-shan Lee . Domain independent key term extraction from spoken content based on context and term location information in the utterances [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2864

Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA


In this paper, bounded generalized Gaussian mixture model (BGGMM) using independent component analysis (ICA) is proposed and applied to an existing unsupervised keyword spotting setting for the generation of posteriorgrams. The ICA mixture model is trained without any transcription information to generate the posteriorgrams which further labels the speech frames of the keyword example(s) and test data.

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Authors:
Nizar Bouguila
Submitted On:
23 February 2016 - 1:44pm
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Unsupervised keyword Spotting_Slides_GlobalSIP_2015.pdf

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[1] Nizar Bouguila, "Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/448. Accessed: Aug. 21, 2018.
@article{448-15,
url = {http://sigport.org/448},
author = {Nizar Bouguila },
publisher = {IEEE SigPort},
title = {Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA},
year = {2015} }
TY - EJOUR
T1 - Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA
AU - Nizar Bouguila
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/448
ER -
Nizar Bouguila. (2015). Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA. IEEE SigPort. http://sigport.org/448
Nizar Bouguila, 2015. Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA. Available at: http://sigport.org/448.
Nizar Bouguila. (2015). "Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA." Web.
1. Nizar Bouguila. Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/448