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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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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  1/2015 - 9

 HIGHLY CITED PAPER 

Enhancing ASR Systems for Under-Resourced Languages through a Novel Unsupervised Acoustic Model Training Technique

CUCU, H. See more information about CUCU, H. on SCOPUS See more information about CUCU, H. on IEEExplore See more information about CUCU, H. on Web of Science, BUZO, A. See more information about  BUZO, A. on SCOPUS See more information about  BUZO, A. on SCOPUS See more information about BUZO, A. on Web of Science, BESACIER, L. See more information about  BESACIER, L. on SCOPUS See more information about  BESACIER, L. on SCOPUS See more information about BESACIER, L. on Web of Science, BURILEANU, C. See more information about BURILEANU, C. on SCOPUS See more information about BURILEANU, C. on SCOPUS See more information about BURILEANU, C. on Web of Science
 
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Download PDF pdficon (612 KB) | Citation | Downloads: 913 | Views: 4,169

Author keywords
speech recognition, under-resourced languages, unsupervised acoustic modeling, unsupervised training

References keywords
speech(15), training(13), unsupervised(12), resourced(5), recognition(5), processing(5), languages(5), language(5), acoustic(5), system(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-02-28
Volume 15, Issue 1, Year 2015, On page(s): 63 - 68
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.01009
Web of Science Accession Number: 000352158600009
SCOPUS ID: 84924787729

Abstract
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Statistical speech and language processing techniques, requiring large amounts of training data, are currently state-of-the-art in automatic speech recognition. For high-resourced, international languages this data is widely available, while for under-resourced languages the lack of data poses serious problems. Unsupervised acoustic modeling can offer a cost and time effective way of creating a solid acoustic model for any under-resourced language. This study describes a novel unsupervised acoustic model training method and evaluates it on speech data in an under-resourced language: Romanian. The key novel factor of the method is the usage of two complementary seed ASR systems to produce high quality transcriptions, with a Character Error Rate (ChER) < 5%, for initially untranscribed speech data. The methodology leads to a relative Word Error Rate (WER) improvement of more than 10% when 100 hours of untranscribed speech are used.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 4 [View]
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Cited-By SCOPUS

SCOPUS® Times Cited: 5
View record in SCOPUS®
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View citations in SCOPUS® [Free preview]

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Cited-By CrossRef

[1] Semi-Supervised Training of Language Model on Spanish Conversational Telephone Speech Data, Egorova, Ekaterina, Serrano, Jordi Luque, Procedia Computer Science, ISSN 1877-0509, Issue , 2016.
Digital Object Identifier: 10.1016/j.procs.2016.04.038
[CrossRef]

[2] Progress on automatic annotation of speech corpora using complementary ASR systems, Georgescu, Alexandru-Lucian, Cucu, Horia, Burileanu, Corneliu, 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), ISBN 978-1-7281-1864-2, 2019.
Digital Object Identifier: 10.1109/TSP.2019.8769087
[CrossRef]

[3] Automatic Annotation of Speech Corpora using Approximate Transcripts, Manolache, Cristian, Georgescu, Alexandru-Lucian, Caranica, Alexandru, Cucu, Horia, 2020 43rd International Conference on Telecommunications and Signal Processing (TSP), ISBN 978-1-7281-6376-5, 2020.
Digital Object Identifier: 10.1109/TSP49548.2020.9163405
[CrossRef]

[4] Automatic Annotation of Speech Corpora Using Complementary GMM and DNN Acoustic Models, Georgescu, Alexandru-Lucian, Cucu, Horia, 2018 41st International Conference on Telecommunications and Signal Processing (TSP), ISBN 978-1-5386-4695-3, 2018.
Digital Object Identifier: 10.1109/TSP.2018.8441374
[CrossRef]

[5] Data-Filtering Methods for Self-Training of Automatic Speech Recognition Systems, Georgescu, Alexandru-Lucian, Manolache, Cristian, Oneata, Dan, Cucu, Horia, Burileanu, Corneliu, 2021 IEEE Spoken Language Technology Workshop (SLT), ISBN 978-1-7281-7066-4, 2021.
Digital Object Identifier: 10.1109/SLT48900.2021.9383577
[CrossRef]

Updated 2 days, 14 hours ago

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