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JCR Impact Factor: 0.459
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PUBLISHER

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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  1/2017 - 4

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: 104 | Views: 150

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

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.


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

[1] C. Zhang, J. H. L. Hansen, "Analysis and Classification of Speech Mode: Whisper through Shouted," in Proc. 8th Annu. Conf. Int. Speech Commun. Assoc. Interspeech 2007, Antwerp, 2007, pp. 2289-2292.

[2] T. Ito, K. Takeda, F. Itakura, "Analysis and recognition of whispered Speech," Speech Communication, vol. 45, pp. 139-152, Feb. 2005.
[CrossRef] [Web of Science Times Cited 58] [SCOPUS Times Cited 96]


[3] D. T. Grozdic, J. Galic, B. Markovic, S. T. Jovicic, "Application of neural networks in whispered speech recognition," Telfor Journal, vol. 5, pp. 103-106, Nov. 2013.

[4] M. E. Ayadi, M. S. Kamel, F. Karray, "Survey on speech emotion recognition: Features, classification schemes and databases," Pattern Recognition, vol. 44, pp. 572-587, Mar. 2011.

[5] H. Boril, J. H. L. Hansen, "UT-Scope: Towards LVCSR under Lombard effect induced by varying types and levels of noisy background," in Proc. IEEE Int. Conf. Acoust. Speech Signal, ICASSP, Prague, 2011, pp. 4472-4475.
[CrossRef] [SCOPUS Times Cited 14]


[6] S. Ghaffarzadegan, H. Boril, J. H. L. Hansen, "UT-Vocal Effort II: Analysis and constrained-lexicon recognition of whispered speech," in Proc. IEEE Int. Conf. Acoust. Speech Signal, ICASSP, Florence, Italy, 2014, pp. 2544-2548.
[CrossRef] [SCOPUS Times Cited 9]


[7] A. Mathur, S. M. Reddy, R. M. Hegde, "Significance of parametric spectral ratio methods in detection and recognition of whispered speech." EURASIP J. Adv. Signal Process., pp. 157-177, Dec. 2012.

[8] C. Y. Yang, G. Brown, L. Lu, J. Yamagishi, S. King, "Noise-robust whispered speech recognition using a non-audible-murmur microphone with VTS compensation," in Proc. 8th International Symposium on Chinese Spoken Language Processing, ISCSLP, Hong Kong, China, 2012, pp. 220-223.
[CrossRef] [SCOPUS Times Cited 10]


[9] R. W. Morris, "Enhancement and recognition of whispered speech," Ph.D. dissertation, School of Electrical and Computer Engineering, Georgia Institute of Technology, August 2003.

[10] D. T. Grozdic, S. T. Jovicic, M. Subotic, "Whispered speech recognition using deep denoising autoencoder," Engineering Applications of Artificial Intelligence, vol. 59, pp. 15-22, Mar. 2017.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]


[11] V. C. Tartter, "What's in a whisper?," Journal of the Acoustical Society of America, vol. 86, 1678-1683, 1989.

[12] B. P. Lim, "Computational differences between whispered and non-whispered speech." Ph.D. thesis, University of Illinois at Urbana-Champaign, 2011.

[13] D. T. Grozdic, S. T. Jovicic, J. Galic, B. Markovic, "Application of inverse filtering in enhancement of whisper recognition," in Proc. 12th Symp. Neural Netw. Appl. Electr. Eng., NEUREL 2014, Belgrade, 2014, pp. 157-161.
[CrossRef] [SCOPUS Times Cited 2]


[14] B. Markovic, S. T. Jovicic, J. Galic, D. T. Grozdic, "Whispered Speech Database: Design, Processing and Application," in Proc. 16th International Conference, TSD 2013, Pilsen, 2013, pp. 591-598.
[CrossRef] [SCOPUS Times Cited 1]


[15] P. X. Lee, D. Wee, H. Si, Y. Toh, B. P. Lim, N. Chen, B. Ma, V.J. College, "A whispered Mandarin corpus for speech technology applications," in Proc. Annu. Conf. Int. Speech Commun. Assoc., INTERSPEECH, Singapore, 2014, pp. 1598-1602.

[16] C. Zhang, J. H. L. Hansen, "Whisper-island detection based on unsupervised segmentation with entropy-based speech feature processing." IEEE Trans. Audio, Speech Lang. Process. 19, 883-894, Aug. 2010.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 21]


[17] S. T. Jovicic, "Formant feature differences between whispered and voiced sustained vowels," Acta Acust., vol. 84 (4), pp. 739-743, Jul. 1998.

[18] H. Boril, J. H. L. Hansen, "Unsupervised equalization of Lombard effect for speech recognition in noisy adverse environments," IEEE Transactions on Audio, Speech, and Language Processing, vol. 18 (6), pp. 1379-1393, Aug. 2010.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 35]


[19] B. Atal, "Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification," J. Acoust. Soc. Am., vol. 55, pp. 1304-1312, 1974.
[CrossRef] [SCOPUS Times Cited 526]


[20] S. J. Hahm, H. Boril, A. Pongtep, J.H.L. Hansen, "Advanced Feature Normalization and Rapid Model Adaptation for Robust In-Vehicle Speech Recognition," in Proc. 6th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems, Seoul, 2013, pp. 14-17.

[21] S. Yoshizawa, N. Hayasaka, N. Wada, Y. Miyanaga, "Cepstral gain normalization for noise robust speech recognition," in Proc. IEEE Int. Conf. Acoust. Speech, Signal Process., Montreal, 2004, pp. 209-212.

[22] N. P. Solomon, G. N. McCall, M. W. Trosset, W. C. Gray, "Laryngeal configuration and constriction during two types of whispering," Journal of Speech and Hearing Research, vol. 32, pp. 161-174, Mar. 1989.

[23] P. Monoson, W. R. Zemlin, "Quantitative study of whisper," Folia Phoniatrica, vol. 36, pp. 53-65, 1984.



References Weight

Web of Science® Citations for all references: 97 TCR
SCOPUS® Citations for all references: 715 TCR

Web of Science® Average Citations per reference: 4 ACR
SCOPUS® Average Citations per reference: 30 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 2017-04-25 09:06 in 78 seconds.




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


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