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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2014 - 9

Post-error Correction in Automatic Speech Recognition Using Discourse Information

KANG, S. See more information about KANG, S. on SCOPUS See more information about KANG, S. on IEEExplore See more information about KANG, S. on Web of Science, KIM, J.-H. See more information about  KIM, J.-H. on SCOPUS See more information about  KIM, J.-H. on SCOPUS See more information about KIM, J.-H. on Web of Science, SEO, J. See more information about SEO, J. on SCOPUS See more information about SEO, J. on SCOPUS See more information about SEO, J. on Web of Science
 
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Download PDF pdficon (654 KB) | Citation | Downloads: 787 | Views: 3,415

Author keywords
post correction, speech recognition, re-ranking model, analysis of user intention, spoken language understanding, spoken dialog system

References keywords
speech(11), recognition(10), language(8), spoken(5), information(5), systems(4), science(4), linguistics(4), computational(4), association(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-05-31
Volume 14, Issue 2, Year 2014, On page(s): 53 - 56
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.02009
Web of Science Accession Number: 000340868100009
SCOPUS ID: 84901838708

Abstract
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Overcoming speech recognition errors in the field of human-computer interaction is important in ensuring a consistent user experience. This paper proposes a semantic-oriented post-processing approach for the correction of errors in speech recognition. The novelty of the model proposed here is that it re-ranks the n-best hypothesis of speech recognition based on the user's intention, which is analyzed from previous discourse information, while conventional automatic speech recognition systems focus only on acoustic and language model scores for the current sentence. The proposed model successfully reduces the word error rate and semantic error rate by 3.65% and 8.61%, respectively.


References | Cited By  «-- Click to see who has cited this paper

[1] S. Kaki, E. Sumita, H. Iida, "A Method for Correcting Errors in Speech Recognition Using the Statistical Features of Character Co-occurrence," in Proc. of Association for Computational Linguistics, pp. 653-657, 1998.
[CrossRef]


[2] R. Lopez-Cozar, Z. Callejas, "ASR Post-Correction for Spoken Dialogue Systems based on Semantic, Syntactic, Lexical and Contextual Information," Speech Communication, vol. 50, no. 8-9, pp. 745-766, 2008.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 16]


[3] J. Allen, B. W. Miller, E. K. Ringger, T. Sikorski, "A Robust System for Natural Spoken Dialog," in Proc. of Association for Computational Linguistics, pp. 62-70, 1996.
[CrossRef]


[4] E. Ringger, J. Allen, "A Fertility Channel Model for Post Correction of Continuous Speech Recognition," in Proc. of International Conference on Spoken Language Processing, pp. 897-900, 1996.
[CrossRef]


[5] M. Jeong, G. G. Lee, "Improving Speech Recognition and Understanding using Error-Corrective Reranking," ACM Transactions on Asian Language Information Processing, vol. 7, pp. 2:1-2:26, 2008.
[CrossRef] [SCOPUS Times Cited 4]


[6] T. Hazen, T. Burianek, J. Polifroni, S. Seneff, "Recognition confidence scoring for use in speech understanding systems," Computer Speech and Language, vol. 16, no. 1, pp. 49-67, 2002.
[CrossRef] [Web of Science Times Cited 61] [SCOPUS Times Cited 96]


[7] T. Baumann, M. Atterer, D. Schlangen, "Assessing and improving the performance of speech recognition for incremental systems," in Proc. Of Association for Computational Linguistics, pp. 380-388, 2009.
[CrossRef] [SCOPUS Times Cited 32]


[8] C. Clavel, G. Adda, Cailliau, M. Garnier-Rizet, A. Cavet, G. Chapuis, S. Courcinous, C. Danesi, A. Daquo, M. Deldossi, S. Guillemin-Lanne, M. Seizou, P. Suignard, "Spontaneous speech and opinion detection: mining call-centre transcripts," Language Resources and Evaluation, 2013.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 11]


[9] J. Vilaneau, J. Y. Antoine, "Deeper Spoken Language Understanding for Man-machine Dialogue on Broader Application Domains: A Logical Alternative to Concept Spotting," in Proc. of Workshop on Semantic Representation of Spoken Language, pp. 50-57, 2009.
[CrossRef]


[10] H. Lee, H. Kim, J. Seo, "Efficient Domain Action Classification using Neural Networks," Lecture Note in Computer Science, vol. 4233, pp. 150-158, 2006.
[CrossRef] [SCOPUS Times Cited 2]


[11] H. Kim, "A Dialogue-based NLIDB System in a Schedule Management Domain: About the Method to Find User's Intentions," in Proc. of conference on Current Trends in Theory and Practice of Computer Science, pp. 869-877, 2007.
[CrossRef] [SCOPUS Times Cited 5]


[12] D. Kim, H. Lee, C. Seon, H. Kim, and J. Seo, "Speakers' Intention Prediction Using Statistics of Multi-level Features in a Schedule Management Domain," in Proc. of Association for Computational Linguistics on Human Language Technologies, pp. 229-232, 2008.
[CrossRef] [SCOPUS Times Cited 2]


[13] H. Kim, C. Seon, J. Seo, "Review of Korean speech act classification: machine learning methods," Journal of Computing Science and Engineering, vol. 5, no 4, pp. 288-293, 2011.
[CrossRef]


[14] V. Vapnik, The Nature of Statistical Learning Theory. Springer Verlag, 1995.
[CrossRef]


[15] L. Bottou, C. Cortes, J. Denker, H. Drucker, I. Guyon, L. Jackel, Y. LeCun, U. Muller, E. Sackinger, P. Simard, and V. Vapnik, "Comparison of Classifier Methods: A Case Study in Handwritten Digit Recognition", in Proc. of International Conference on Pattern Recognition, vol. 2, pp. 77-82, 1994.
[CrossRef]


[16] S. Kang, H. Kim, J. Seo, "A Reliable Multidomain Model for Speech Act Classification," Pattern Recognition Letters, vol. 31, no 1, pp. 71-74, 2010.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 8]


[17] C. Seon, H. Kim, J. Seo, "Efficient Appointment Information Extraction from Messages in Mobile Devices with Limited Hardware Resources," Pattern Recognition Letters, vol. 32, no 2, pp. 127-133, 2011.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]


[18] R. Nallapati, "Discriminative Models for Information Retrieval," in Proc. of SIGIR, pp. 64-71, 2004.
[CrossRef] [SCOPUS Times Cited 215]


[19] K. Lee, M. Chung, "Morpheme-Based Modeling of Pronunciation Variation for Large Vocabulary Continuous Speech Recognition in Korean," IEICE Transaction on Information and Systems, vol. E90-D, no. 7, pp. 1063-1072, 2004.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 10]


[20] M. Lee, D. Han, "Ubiscript: A Script Language for Ubiquitous Environment," Journal of Computing Science and Engineering, vol. 5, no 2, pp. 141-149, 2011
[CrossRef]




References Weight

Web of Science® Citations for all references: 101 TCR
SCOPUS® Citations for all references: 405 TCR

Web of Science® Average Citations per reference: 5 ACR
SCOPUS® Average Citations per reference: 19 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-05-12 08:15 in 122 seconds.




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