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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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Comparison of Cepstral Normalization Techniques in Whispered Speech Recognition

GROZDIC, D. See more information about GROZDIC, D. on SCOPUS See more information about GROZDIC, D. on IEEExplore See more information about GROZDIC, D. on Web of Science, JOVICIC, S. See more information about  JOVICIC, S. on SCOPUS See more information about  JOVICIC, S. on SCOPUS See more information about JOVICIC, S. on Web of Science, SUMARAC PAVLOVIC, D. See more information about  SUMARAC PAVLOVIC, D. on SCOPUS See more information about  SUMARAC PAVLOVIC, D. on SCOPUS See more information about SUMARAC PAVLOVIC, D. on Web of Science, GALIC, J. See more information about  GALIC, J. on SCOPUS See more information about  GALIC, J. on SCOPUS See more information about GALIC, J. on Web of Science, MARKOVIC, B. See more information about MARKOVIC, B. on SCOPUS See more information about MARKOVIC, B. on SCOPUS See more information about MARKOVIC, B. on Web of Science
 
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Download PDF pdficon (1,179 KB) | Citation | Downloads: 732 | Views: 2,297

Author keywords
automatic speech recognition, cepstral analysis, hidden Markov models, speech analysis, whisper

References keywords
speech(26), recognition(13), whispered(12), hansen(6), whisper(5), signal(5), processing(5), jovicic(5), grozdic(4), boril(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-02-28
Volume 17, Issue 1, Year 2017, On page(s): 21 - 26
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.01004
Web of Science Accession Number: 000396335900004
SCOPUS ID: 85014204959

Abstract
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This article presents an analysis of different cepstral normalization techniques in automatic recognition of whispered and bimodal speech (speech+whisper). In these experiments, conventional GMM-HMM speech recognizer was used as speaker-dependant automatic speech recognition system with special Whi-Spe corpus containing utterance recordings in normally phonated speech and whisper. The following normalization techniques were tested and compared: CMN (Cepstral Mean Normalization), CVN (Cepstral Variance Normalization), MVN (Cepstral Mean and Variance Normalization), CGN (Cepstral Gain Normalization) and quantile-based dynamic normalization techniques such as QCN and QCN-RASTA. The experimental results show to what extent each of these cepstral normalization techniques can improve whisper recognition accuracy in mismatched train/test scenario. The best result is obtained using CMN in combination with inverse filtering which provides an average 39.9 percent improvement in whisper recognition accuracy for all tested speakers.


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Cited-By SCOPUS

SCOPUS® Times Cited: 5
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Cited-By CrossRef

[1] Performance Evaluation of Normalization Techniques in Adverse Conditions, Singh, Renu, Bhattacharjee, Utpal, Singh, Arvind Kumar, Procedia Computer Science, ISSN 1877-0509, Issue , 2020.
Digital Object Identifier: 10.1016/j.procs.2020.04.169
[CrossRef]

[2] Research of Window Function Influence on the Result of Arabic Speech Automatic Recognition, Levin, Evgenii, Al-Dhaibani, Abdulghani, 2019 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT), ISBN 978-1-5386-8364-4, 2019.
Digital Object Identifier: 10.1109/USBEREIT.2019.8736574
[CrossRef]

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


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