Spoken document retrieval by discriminative modeling in a high dimensional feature space

Takanobu Oba, Takaaki Hori, Atsushi Nakamura, Akinori Ito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes discriminative modeling in a high dimensional feature space for spoken document retrieval (SDR). To estimate the parameters of a high dimensional model properly, a large quantity of data is necessary, but there is no such large corpus for document retrieval. This paper employs two approaches to overcome this problem. One is a reranking approach. A baseline system first gives each document a score and then the score is compensated by employing a high dimensional model. The other approach is automatic query generation. A large number of queries are automatically generated and used for parameter estimation. Our experimental result shows that our proposed method can greatly improve SDR performance.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages5153-5156
Number of pages4
DOIs
Publication statusPublished - 2012 Oct 23
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

Other

Other2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
CountryJapan
CityKyoto
Period12/3/2512/3/30

Keywords

  • Discriminative model
  • Linear model
  • Spoken document retrieval

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Oba, T., Hori, T., Nakamura, A., & Ito, A. (2012). Spoken document retrieval by discriminative modeling in a high dimensional feature space. In 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings (pp. 5153-5156). [6289080] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2012.6289080