Round-robin duel discriminative language models in one-pass decoding with on-the-fly error correction

Takanobu Oba, Takaaki Hori, Akinori Ito, Atsushi Nakamura

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

4 Citations (Scopus)

Abstract

This paper focuses on discriminative n-gram language models for large vocabulary speech recognition. We have proposed a novel training method called the round-robin duel discrimination (R2D2) method. Our previous report showed that R2D2 outperforms conventional methods on word n-gram based discriminative language models (DLMs). In this paper, we achieve additional error reduction and one-pass decoding at the same time. The keys to achieving this are the use of morphological features and the on-the-fly composition of weighted finite-state transducers (WFSTs) that represent both word and morphological discriminative features. Our experimental results show that R2D2 can reduce recognition errors more effectively than conventional methods in the reranking of n-best hypotheses and one-pass decoding can be accomplished with an equivalent accuracy.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages5588-5591
Number of pages4
DOIs
Publication statusPublished - 2011 Aug 18
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 2011 May 222011 May 27

Publication series

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

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
CountryCzech Republic
CityPrague
Period11/5/2211/5/27

Keywords

  • Discriminative language model
  • Error correction
  • On-the-fly algorithm
  • R2D2
  • WFST

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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