A discriminative model for continuous speech recognition based on weighted finite state transducers

Shinji Watanabe, Takaaki Hori, Erik McDermott, Atsushi Nakamura

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

3 Citations (Scopus)

Abstract

This paper proposes a discriminative model for speech recognition that directly optimizes the parameters of a speech model represented in the form of a decoding graph. In the process of recognition, a decoder, given an input speech signal, searches for an appropriate label sequence among possible combinations from separate knowledge sources of speech, e.g., acoustic, lexicon, and language models. It is more reasonable to use an integrated knowledge source, which is composed of these models and forms an overall space to be searched by a decoder, than to use separate ones. This paper aims to estimate a speech model composed in this way directly in the search network, unlike discriminative training approaches, which estimate parameters in acoustic or language model layers. Our approach is formulated as the weight parameter optimization of log-linear distributions in the decoding arcs of a Weighted Finite State Transducer (WFST) to efficiently handle a large network statically. The weight parameters are estimated by an averaged perceptron algorithm. The experimental results show that, especially when the model size is small, the proposed approach provided better recognition performance than the conventional maximum likelihood and comparable to or slightly better performance than discriminative training approaches.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages4922-4925
Number of pages4
DOIs
Publication statusPublished - 2010 Nov 8
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: 2010 Mar 142010 Mar 19

Publication series

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

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period10/3/1410/3/19

Keywords

  • Averaged perceptron
  • Discriminative model
  • Loglinear model
  • Parameter optimization in a decoding graph
  • Speech recognition
  • Weighted finite state transducer

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Watanabe, S., Hori, T., McDermott, E., & Nakamura, A. (2010). A discriminative model for continuous speech recognition based on weighted finite state transducers. In 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings (pp. 4922-4925). [5495096] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2010.5495096