Search error risk minimization in viterbi beam search for speech recognition

Takaaki Hori, Shinji Watanabe, Atsushi Nakamura

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

2 Citations (Scopus)

Abstract

This paper proposes a method to optimize Viterbi beam search based on search error risk minimization in large vocabulary continuous speech recognition (LVCSR). Most speech recognizers employ beam search to speed up the decoding process, in which unpromising partial hypotheses are pruned during decoding. However, the pruning step involves the risk of missing the best complete hypothesis by discarding a partial hypothesis that might grow into the best. Missing the best hypothesis is called search error. Our purpose is to reduce search error by optimizing the pruning step. While conventional methods use heuristic criteria to prune each hypothesis based on its score, rank, and so on, our proposed method introduces a pruning function that makes a more precise decision using the rich features extracted from each hypothesis. The parameters of the function can be estimated efficiently to minimize the search error risk using recognition lattices at the training step. We implemented the new method in a WFST-based decoder and achieved a significant reduction of search errors in a 200K-word LVCSR task.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages4934-4937
Number of pages4
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX
Duration: 2010 Mar 142010 Mar 19

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CityDallas, TX
Period10/3/1410/3/19

Fingerprint

Speech recognition
Continuous speech recognition
Decoding

Keywords

  • Pruning
  • Search error
  • Viterbi beam search
  • WFST

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Cite this

Hori, T., Watanabe, S., & Nakamura, A. (2010). Search error risk minimization in viterbi beam search for speech recognition. In 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings (pp. 4934-4937). [5495099] https://doi.org/10.1109/ICASSP.2010.5495099

Search error risk minimization in viterbi beam search for speech recognition. / Hori, Takaaki; Watanabe, Shinji; Nakamura, Atsushi.

2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings. 2010. p. 4934-4937 5495099.

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

Hori, T, Watanabe, S & Nakamura, A 2010, Search error risk minimization in viterbi beam search for speech recognition. in 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings., 5495099, pp. 4934-4937, 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010, Dallas, TX, 10/3/14. https://doi.org/10.1109/ICASSP.2010.5495099
Hori T, Watanabe S, Nakamura A. Search error risk minimization in viterbi beam search for speech recognition. In 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings. 2010. p. 4934-4937. 5495099 https://doi.org/10.1109/ICASSP.2010.5495099
Hori, Takaaki ; Watanabe, Shinji ; Nakamura, Atsushi. / Search error risk minimization in viterbi beam search for speech recognition. 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings. 2010. pp. 4934-4937
@inproceedings{4316dffac8414bf1bdc4e0a48847aefb,
title = "Search error risk minimization in viterbi beam search for speech recognition",
abstract = "This paper proposes a method to optimize Viterbi beam search based on search error risk minimization in large vocabulary continuous speech recognition (LVCSR). Most speech recognizers employ beam search to speed up the decoding process, in which unpromising partial hypotheses are pruned during decoding. However, the pruning step involves the risk of missing the best complete hypothesis by discarding a partial hypothesis that might grow into the best. Missing the best hypothesis is called search error. Our purpose is to reduce search error by optimizing the pruning step. While conventional methods use heuristic criteria to prune each hypothesis based on its score, rank, and so on, our proposed method introduces a pruning function that makes a more precise decision using the rich features extracted from each hypothesis. The parameters of the function can be estimated efficiently to minimize the search error risk using recognition lattices at the training step. We implemented the new method in a WFST-based decoder and achieved a significant reduction of search errors in a 200K-word LVCSR task.",
keywords = "Pruning, Search error, Viterbi beam search, WFST",
author = "Takaaki Hori and Shinji Watanabe and Atsushi Nakamura",
year = "2010",
doi = "10.1109/ICASSP.2010.5495099",
language = "English",
isbn = "9781424442966",
pages = "4934--4937",
booktitle = "2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings",

}

TY - GEN

T1 - Search error risk minimization in viterbi beam search for speech recognition

AU - Hori, Takaaki

AU - Watanabe, Shinji

AU - Nakamura, Atsushi

PY - 2010

Y1 - 2010

N2 - This paper proposes a method to optimize Viterbi beam search based on search error risk minimization in large vocabulary continuous speech recognition (LVCSR). Most speech recognizers employ beam search to speed up the decoding process, in which unpromising partial hypotheses are pruned during decoding. However, the pruning step involves the risk of missing the best complete hypothesis by discarding a partial hypothesis that might grow into the best. Missing the best hypothesis is called search error. Our purpose is to reduce search error by optimizing the pruning step. While conventional methods use heuristic criteria to prune each hypothesis based on its score, rank, and so on, our proposed method introduces a pruning function that makes a more precise decision using the rich features extracted from each hypothesis. The parameters of the function can be estimated efficiently to minimize the search error risk using recognition lattices at the training step. We implemented the new method in a WFST-based decoder and achieved a significant reduction of search errors in a 200K-word LVCSR task.

AB - This paper proposes a method to optimize Viterbi beam search based on search error risk minimization in large vocabulary continuous speech recognition (LVCSR). Most speech recognizers employ beam search to speed up the decoding process, in which unpromising partial hypotheses are pruned during decoding. However, the pruning step involves the risk of missing the best complete hypothesis by discarding a partial hypothesis that might grow into the best. Missing the best hypothesis is called search error. Our purpose is to reduce search error by optimizing the pruning step. While conventional methods use heuristic criteria to prune each hypothesis based on its score, rank, and so on, our proposed method introduces a pruning function that makes a more precise decision using the rich features extracted from each hypothesis. The parameters of the function can be estimated efficiently to minimize the search error risk using recognition lattices at the training step. We implemented the new method in a WFST-based decoder and achieved a significant reduction of search errors in a 200K-word LVCSR task.

KW - Pruning

KW - Search error

KW - Viterbi beam search

KW - WFST

UR - http://www.scopus.com/inward/record.url?scp=78049370808&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78049370808&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2010.5495099

DO - 10.1109/ICASSP.2010.5495099

M3 - Conference contribution

SN - 9781424442966

SP - 4934

EP - 4937

BT - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings

ER -