Handling uncertain observations in unsupervised topic-mixture language model adaptation

Ekapol Chuangsuwanich; Shinji Watanabe; Takaaki Hori; Tomoharu Iwata; James Glass

doi:10.1109/ICASSP.2012.6289051

Handling uncertain observations in unsupervised topic-mixture language model adaptation

Ekapol Chuangsuwanich^*, Shinji Watanabe, Takaaki Hori, Tomoharu Iwata, James Glass

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

We propose an extension to the recent approaches in topic-mixture modeling such as Latent Dirichlet Allocation and Topic Tracking Model for the purpose of unsupervised adaptation in speech recognition. Instead of using the 1-best input given by the speech recognizer, the proposed model takes confusion network as an input to alleviate recognition errors. We incorporate a selection variable which helps reweight the recognition output, thus creating a more accurate latent topic estimate. Compared to adapting based on just one recognition hypothesis, the proposed model show WER improvements on two different tasks.

Original language	English
Title of host publication	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages	5033-5036
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2012.6289051
Publication status	Published - 2012
Externally published	Yes
Event	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan Duration: 2012 Mar 25 → 2012 Mar 30

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)	1520-6149

Conference

Conference	2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/Territory	Japan
City	Kyoto
Period	12/3/25 → 12/3/30

Keywords

confusion network
language model
latent topic model
topic tracking

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2012.6289051

Cite this

Chuangsuwanich, E., Watanabe, S., Hori, T., Iwata, T., & Glass, J. (2012). Handling uncertain observations in unsupervised topic-mixture language model adaptation. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 5033-5036). [6289051] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6289051

Handling uncertain observations in unsupervised topic-mixture language model adaptation. / Chuangsuwanich, Ekapol; Watanabe, Shinji; Hori, Takaaki et al.
2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. p. 5033-5036 6289051 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chuangsuwanich, E, Watanabe, S, Hori, T, Iwata, T & Glass, J 2012, Handling uncertain observations in unsupervised topic-mixture language model adaptation. in 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings., 6289051, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 5033-5036, 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012, Kyoto, Japan, 12/3/25. https://doi.org/10.1109/ICASSP.2012.6289051

Chuangsuwanich E, Watanabe S, Hori T, Iwata T, Glass J. Handling uncertain observations in unsupervised topic-mixture language model adaptation. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. p. 5033-5036. 6289051. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2012.6289051

Chuangsuwanich, Ekapol ; Watanabe, Shinji ; Hori, Takaaki et al. / Handling uncertain observations in unsupervised topic-mixture language model adaptation. 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings. 2012. pp. 5033-5036 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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abstract = "We propose an extension to the recent approaches in topic-mixture modeling such as Latent Dirichlet Allocation and Topic Tracking Model for the purpose of unsupervised adaptation in speech recognition. Instead of using the 1-best input given by the speech recognizer, the proposed model takes confusion network as an input to alleviate recognition errors. We incorporate a selection variable which helps reweight the recognition output, thus creating a more accurate latent topic estimate. Compared to adapting based on just one recognition hypothesis, the proposed model show WER improvements on two different tasks.",

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Handling uncertain observations in unsupervised topic-mixture language model adaptation

Abstract

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Keywords

ASJC Scopus subject areas

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