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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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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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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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  3/2012 - 12

Automatic Recognition of Improperly Pronounced Initial 'r' Consonant in Romanian

VELICAN, V. See more information about VELICAN, V. on SCOPUS See more information about VELICAN, V. on IEEExplore See more information about VELICAN, V. on Web of Science, STRUNGARU, R. See more information about  STRUNGARU, R. on SCOPUS See more information about  STRUNGARU, R. on SCOPUS See more information about STRUNGARU, R. on Web of Science, GRIGORE, O. See more information about GRIGORE, O. on SCOPUS See more information about GRIGORE, O. on SCOPUS See more information about GRIGORE, O. 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 (856 KB) | Citation | Downloads: 360 | Views: 2,040

Author keywords
speech processing, pronunciation evaluation

References keywords
speech(16), link(9), grigore(8), therapy(7), impaired(6), velican(5), system(5), schipor(5), recognition(5), language(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2012-08-31
Volume 12, Issue 3, Year 2012, On page(s): 79 - 84
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.03012
Web of Science Accession Number: 000308290500012
SCOPUS ID: 84865836127

Abstract
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Full text preview
Correctly assessing the degree of mispronunciation and deciding upon the necessary treatment are fundamental activities for all speech disorder specialists. Obviously, the experience and the availability of the specialists are essentials in order to assure an efficient therapy for the speech impaired. To overcome this deficiency a more objective approach would include the existence of a tool that independent of the specialist's abilities could be used to establish the diagnostics. A complete automated system based on speech processing algorithms capable of performing the recognition task is therefore thoroughly justified and can be viewed as a goal that will bring many benefits to the field of speech pronunciation correction. This paper presents further results of the authors' work on developing speech processing algorithms able to identify mispronunciations in Romanian language, more exactly we propose the use of the Walsh-Hadamard Transform (WHT) as feature selection tool in the case of identifying rhotacism. The results are encouraging with a best recognition rate of 92.55%.


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

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[2] G. Potamianos, C. Neti, "Automatic Speechreading of Impaired Speech", in Proceedings of the Audio-Visual Speech Processing Workshop, Scheelsminde, Denmark, 2001.

[3] T. Starner, J. Weaver, A. Pentland, "Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video", in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, no. 12, december 1998, pp. 1371 - 1375.
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[5] V. Young, A. Mihailidis, "Difficulties in Automatic Speech Recognition of Dysarthric Speakers and Implications for Speech-Based Applications Used by the Elderly: A Literature Review", in Assistive Technology: The Official Journal of RESNA, 2010, Vol.22, Issue 2, pp. 99-112.
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[CrossRef] [Full Text] [Web of Science Times Cited 8] [SCOPUS Times Cited 12]


[7] O. A. Schipor, S.-G. Pentiuc, M.D. Schipor, "Improving Computer Based Speech Therapy Using a Fuzzy Expert System", in Computing and Informatics, Vol. 22, 2003. [Online] Available: Temporary on-line reference link removed - see the PDF document

[8] M. Danubianu, S.-G. Pentiuc, O.A. Schipor, M. Nestor, I. Ungureanu, "Distributed Intelligent System for Personalized Therapy of Speech Disorders", in The Third International Multi-Conference on Computing in the Global Information Technology, 2008, Athens, Greece.

[9] Gladiola Andruseac, H. Costin, C. Rotariu, "eLearning Platform for Rehabilitation of the Romanian Patients with Neurological Diseases," in Proc. of ICIW 2009, Fourth Int. Conf. on Internet and Web Applications and Services, Venice, Italy, May 2009, pp.573-577, ISBN: 978-0-7695-3613-2

[10] Gladiola Andruseac, H. Costin, C. Rotariu, "Design of a Virtual Learning Environmnent for Romanian Patients with Dyslexia" in Proc. of 1st International Conference on Computer Supported Education, CSEDU 2009, 23-26 March 2009, Lisbon, Portugal, vol. II, pp. 301-304, ISBN: 978-989-8111-83-8

[11] O. Grigore, C. Grigore, V. Velican, "Intelligent System for Impaired Speech Evaluation", in Proceedings of the International Conference on Circuits, Systems, Signals, 10/2010, pp. 365-368. [Online] Available: Temporary on-line reference link removed - see the PDF document

[12] O. Grigore, C. Grigore, V. Velican, "Impaired Speech Evaluation using Mel-Cepstrum Analysis", in International Journal Of Circuits, Systems And Signal Processing, pp. 70-77. [Online] Available: Temporary on-line reference link removed - see the PDF document

[13] O. Grigore, V. Velican, "Pattern Recognition Based Method Used in Identifying Impaired Speech", in Proceedings of the 2nd International Conference on Applied Informatics and Computing Theory (AICT '11): Recent Researches in Applied Informatics, 2011, Prague - Czech Republic, pp. 190-194, [Online] Available: Temporary on-line reference link removed - see the PDF document

[14] I. Gavat, O. Grigore, V. Velican, " Impaired Speech Recognition. Case Study: Recognition of Initial 'r' Consonant in Rhotacsim Affected Pronunciations", in Proceedings of the 6th Conference on Speech Technology and Human-Computer Dialogue (SpeD), 2011, Brasov, Romania, pp.1-6.

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[16] D. V. Popovici, C. Buica-Belciu, V.Velican, "From ASR to CAST: Intelligent Systems for the Diagnosis and Therapy of Speech-Language Disorders", in Revista de Psihopedagogie, 2010, Issue 2, pp.25-37.

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[24] T.-.H. Cho, R. Conners, P. Araman, "A Comparison of Rule-Based, K-Nearest Neighbor, and Neural Net Classifiers for Automated Industrial Inspection", in Proceedings of the IEEE/ACM International Conference on Developing and Managing Expert System Programs, 1991, Washington, USA, pp. 202 - 209.

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[26] W. Wang, S. Li, C. Wang, ICL at NTCIR-7: "An Improved KNN Algorithm for Text Categorization", in Proceedings of NTCIR-7 Workshop Meeting, 2008, Tokyo, Japan, pp.385-388.[Online] Available: Temporary on-line reference link removed - see the PDF document

[27] R. Hassan, M. Hossain, J. Bailey, "Improving k-Nearest Neighbour Classification with Distance Functions Based on Receiver Operating Characteristics", in Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases, 2008, Berlin, Germany, pp.489 - 504.
[CrossRef] [SCOPUS Times Cited 11]


References Weight

Web of Science® Citations for all references: 458 TCR
SCOPUS® Citations for all references: 679 TCR

Web of Science® Average Citations per reference: 17 ACR
SCOPUS® Average Citations per reference: 25 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 2017-10-19 17:53 in 31 seconds.




Note1: Web of Science® is a registered trademark of Thomson Reuters.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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


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