Candidate generation for ASR output error correction using a Context-Dependent Syllable Cluster-based Confusion Matrix

Chao Hong Liu; Chung Hsien Wu; David Sarwono; Jhing Fa Wang

Candidate generation for ASR output error correction using a Context-Dependent Syllable Cluster-based Confusion Matrix

Chao Hong Liu, Chung Hsien Wu, David Sarwono, Jhing Fa Wang

Department of Computer Science and Information Engineering

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

Error correction techniques have been proposed in the applications of language learning and spoken dialogue systems for spoken language understanding. These techniques include two consecutive stages: the generation of correction candidates and the selection of correction candidates. In this study, a Context-Dependent Syllable Cluster (CD-SC)-based Confusion Matrix is proposed for the generation of correction candidates. A Contextual Fitness Score, measuring the sequential relationship to the neighbors of the candidate, is proposed for corrected syllable sequence selection. Finally, the n-gram language model is used to determine the final word sequence output. Experiments show that the proposed method improved from 0.742 to 0.771 in terms of BLEU score as compared to the conventional speech recognition mechanism.

Original language	English
Pages (from-to)	1633-1636
Number of pages	4
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication status	Published - 2011
Event	12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy Duration: 2011 Aug 27 → 2011 Aug 31

All Science Journal Classification (ASJC) codes

Language and Linguistics
Human-Computer Interaction
Signal Processing
Software
Modelling and Simulation

Cite this

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title = "Candidate generation for ASR output error correction using a Context-Dependent Syllable Cluster-based Confusion Matrix",

abstract = "Error correction techniques have been proposed in the applications of language learning and spoken dialogue systems for spoken language understanding. These techniques include two consecutive stages: the generation of correction candidates and the selection of correction candidates. In this study, a Context-Dependent Syllable Cluster (CD-SC)-based Confusion Matrix is proposed for the generation of correction candidates. A Contextual Fitness Score, measuring the sequential relationship to the neighbors of the candidate, is proposed for corrected syllable sequence selection. Finally, the n-gram language model is used to determine the final word sequence output. Experiments show that the proposed method improved from 0.742 to 0.771 in terms of BLEU score as compared to the conventional speech recognition mechanism.",

author = "Liu, {Chao Hong} and Wu, {Chung Hsien} and David Sarwono and Wang, {Jhing Fa}",

year = "2011",

language = "English",

pages = "1633--1636",

journal = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",

issn = "2308-457X",

note = "12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 ; Conference date: 27-08-2011 Through 31-08-2011",

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Candidate generation for ASR output error correction using a Context-Dependent Syllable Cluster-based Confusion Matrix. / Liu, Chao Hong; Wu, Chung Hsien; Sarwono, David et al.
In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2011, p. 1633-1636.

Research output: Contribution to journal › Conference article › peer-review

TY - JOUR

T1 - Candidate generation for ASR output error correction using a Context-Dependent Syllable Cluster-based Confusion Matrix

AU - Liu, Chao Hong

AU - Wu, Chung Hsien

AU - Sarwono, David

AU - Wang, Jhing Fa

PY - 2011

Y1 - 2011

N2 - Error correction techniques have been proposed in the applications of language learning and spoken dialogue systems for spoken language understanding. These techniques include two consecutive stages: the generation of correction candidates and the selection of correction candidates. In this study, a Context-Dependent Syllable Cluster (CD-SC)-based Confusion Matrix is proposed for the generation of correction candidates. A Contextual Fitness Score, measuring the sequential relationship to the neighbors of the candidate, is proposed for corrected syllable sequence selection. Finally, the n-gram language model is used to determine the final word sequence output. Experiments show that the proposed method improved from 0.742 to 0.771 in terms of BLEU score as compared to the conventional speech recognition mechanism.

AB - Error correction techniques have been proposed in the applications of language learning and spoken dialogue systems for spoken language understanding. These techniques include two consecutive stages: the generation of correction candidates and the selection of correction candidates. In this study, a Context-Dependent Syllable Cluster (CD-SC)-based Confusion Matrix is proposed for the generation of correction candidates. A Contextual Fitness Score, measuring the sequential relationship to the neighbors of the candidate, is proposed for corrected syllable sequence selection. Finally, the n-gram language model is used to determine the final word sequence output. Experiments show that the proposed method improved from 0.742 to 0.771 in terms of BLEU score as compared to the conventional speech recognition mechanism.

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M3 - Conference article

AN - SCOPUS:84865745770

SN - 2308-457X

SP - 1633

EP - 1636

JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

T2 - 12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011

Y2 - 27 August 2011 through 31 August 2011

ER -

Candidate generation for ASR output error correction using a Context-Dependent Syllable Cluster-based Confusion Matrix

Abstract

All Science Journal Classification (ASJC) codes

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