|1/2017 - 4|
Comparison of Cepstral Normalization Techniques in Whispered Speech RecognitionGROZDIC, D. , JOVICIC, S. , SUMARAC PAVLOVIC, D. , GALIC, J. , MARKOVIC, B.
|View the paper record and citations in|
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,179 KB) | Citation | Downloads: 1,018 | Views: 2,988|
automatic speech recognition, cepstral analysis, hidden Markov models, speech analysis, whisper
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
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|
| 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.
 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 114] [SCOPUS Times Cited 152]
 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.
 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.
 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 22]
 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 27]
 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.
 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 26]
 R. W. Morris, "Enhancement and recognition of whispered speech," Ph.D. dissertation, School of Electrical and Computer Engineering, Georgia Institute of Technology, August 2003.
 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 48] [SCOPUS Times Cited 58]
 V. C. Tartter, "What's in a whisper?," Journal of the Acoustical Society of America, vol. 86, 1678-1683, 1989.
 B. P. Lim, "Computational differences between whispered and non-whispered speech." Ph.D. thesis, University of Illinois at Urbana-Champaign, 2011.
 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 9]
 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 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.
 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 36] [SCOPUS Times Cited 39]
 S. T. Jovicic, "Formant feature differences between whispered and voiced sustained vowels," Acta Acust., vol. 84 (4), pp. 739-743, Jul. 1998.
 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 54] [SCOPUS Times Cited 64]
 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 710]
 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.
 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.
 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.
 P. Monoson, W. R. Zemlin, "Quantitative study of whisper," Folia Phoniatrica, vol. 36, pp. 53-65, 1984.
Web of Science® Citations for all references: 252 TCR
SCOPUS® Citations for all references: 1,122 TCR
Web of Science® Average Citations per reference: 11 ACR
SCOPUS® Average Citations per reference: 47 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 2023-09-29 17:06 in 61 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
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.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.