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JCR Impact Factor: 0.595
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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

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


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2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
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2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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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
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (654 KB) | Citation | Downloads: 287 | Views: 1,715

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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Full text preview
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

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[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 9] [SCOPUS Times Cited 13]


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[CrossRef]


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[CrossRef]


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[CrossRef] [SCOPUS Times Cited 3]


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[CrossRef] [Web of Science Times Cited 54] [SCOPUS Times Cited 82]


[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]


[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 5] [SCOPUS Times Cited 8]


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[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]


[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]


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[CrossRef]


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[CrossRef]


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[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 5] [SCOPUS Times Cited 5]


[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.
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[CrossRef]


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[CrossRef]




References Weight

Web of Science® Citations for all references: 84 TCR
SCOPUS® Citations for all references: 122 TCR

Web of Science® Average Citations per reference: 4 ACR
SCOPUS® Average Citations per reference: 6 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 2017-08-18 22:57 in 136 seconds.




Note1: Web of Science® is a registered trademark of Thomson Reuters.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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