Handling uncertain observations in unsupervised topic-mixture language model adaptation

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages5033-5036
Number of pages4
DOIs
Publication statusPublished - 2012 Oct 23
Externally publishedYes
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 2012 Mar 252012 Mar 30

Publication series

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

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period12/3/2512/3/30

Keywords

  • confusion network
  • language model
  • latent topic model
  • topic tracking

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Handling uncertain observations in unsupervised topic-mixture language model adaptation'. Together they form a unique fingerprint.

  • 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