Acoustic-Phonetic Feature Based Dialect Identification in Hindi Speech
By:
References
- R. Huang, J. H. L. Hansen and P. Angkititrakul, “Dialect/Accent Classification using Unrestricted Audio”, IEEE Transaction on Audio, Speech and Language Processing, 15(2), pp. 453-464, 2007.
- J. C. Wells, “Accent of English”, 1982, VOL. 2; Cambridge University Press, Landon.
- S. Sinha, S. S. Agrawal and A. Jain, “Dialectal influences on acoustic duration of Hindi phonemes”, Proceeding of International Conference of The International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques (OCOCOSDA), November 25-27, 2013. pp. 1-5.
- D. Mishra and K. Bali,”A comparative phonological study of the dialects of Hindi”, in Proceedings of International Congress of Phonetic Sciences XVII , August 17-21, 2011, pp. 1390-1393.
- A. H. M. Russell and M. Carey, “Human and computer recognition of regional accents and ethnic groups from British English speech”, Computer Speech and Language, 27(1), pp. 59-74, 2013.
- E. L. Goh, “Gender and accent identification for Malaysian English using MFCC and Gaussian mixture model”, Doctoral dissertation, Faculty of Computing, Universiti Teknologi, Malaysia, 2013.
- P. Dhanalakshmi,S. Palanivel and V. Ramalingam, “Classification of audio signals using AANN and GMM”, Applied Soft Computing, 11(1), pp.716-723, 2011.
- A. Waibel, “Prosody and speech recognition”. Morgan Kaufmann, 1988.
- K. Sreenivasa Rao, “Role of neural network models for developing speech systems”, Sadhana, 36(5), pp. 783-836, 2011.
- S. Gray and J.H.L. Hansen, “An integrated approach to the detection and classification of accents/dialects for a spoken document retrieval system”. IEEE Workshop on Automatic Speech Recognition and Understanding, November 27- December 1, 2005, pp. 35-40.
- A.S. Ghotkar and G. K. Kharate, “Study of vision based hand gesture recognition using Indian sign language”, International Journal on Smart Sensing and Intelligent Systems, 7(1), pp. 96-115, March 2014.
- K. S. Rao, and S. G. Koolagudi, “Identification of Hindi dialects and emotions using spectral and prosodic features of speech”. International Journal of Systemics, Cybernetics and Informatics, 9(4), pp. 24-33, 2011.
- S. Sinha, A. Jain and S. S. Agrawal, “Speech Processing for Hindi Dialect Recognition”. Advances in Signal Processing and Intelligent Recognition Systems, Vol 264, pp. 161-169, 2014.
- R.K. Aggarwal and M. Dave, “Integration of multiple acoustic and language models for improved Hindi speech recognition system”, International Journal of Speech Technology, 15(2), pp. 165-180, 2012.
- R.K. Aggarwal and M. Dave, “Performance evaluation of sequentially combined heterogeneous feature streams for Hindi speech recognition system”, Telecommunication Systems, 52(3), pp. 1457-1466, 2013.
- A. Jansen and P. Niyogi, “A geometric perspective on speech sounds”, Tech. Rep. TR- 2004-06, University of Chicago, June 2005.
- A. Errity and J. McKenna, “A comparision of linear and nonlinear dimensionality reduction methods applied to synthetic speech”, Proceedings of the Annual Conference of International Speech Communication Association (INTERSPEECH), Brighton, September 6-10, 2009, pp. 1095-1098.
- Ma Zongming, “Sparse principal component analysis and iterative thresholding”, The Annals of Statistics, 41(2), pp. 772-801, 2013.
- A. Zolnay et al., “Using multiple acoustic feature sets for speech recognition”. Speech Communication, 49(6), pp. 514-525, 2007.
- A. Che Soh, K.K.Chow, U. K. Mohammad Yusuf, A. J. Ishak, M. K. Hassan, S.Khamis, “Development of neural network-based electronic nose for herbs recognition”, International Journal on Smart Sensing and Intelligent Systems,7(2), pp. 584-609, June 2014.
- M. A. Kramer, “Nonlinear principal component analysis using autoassociative neural networks”. AIChE journal, Wiley online, 37(2), pp. 233-243, 1991.
- K. Sreenivasa Rao, D. Nandi and S. G. Koolagudi. “Film segmentation and indexing using autoassociative neural networks.” International Journal of Speech Technology, 17(1), pp. 65-74, 2014.
- S. Davis and P. Mermelstein, “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences”. IEEE Transactions on Acoustics, Speech and Signal Processing, 28(4), pp. 357-366, 1980.
- A. N. Mishra, M. Chandra, A. Biswas and S. N. Sharan, “Robust features for connected Hindi digits recognition”. International Journal of Signal Processing, Image Processing and Pattern Recognition, 4(2), 79-90, 2011.
- Marie-José Kolly and Volker Dellwo, “Cues to linguistic origin: The contribution of speech temporal information to foreign accent recognition”, Journal of Phonetics, Vol. 42, pp. 12-23, 2014.
- A. Gaddam, G. Sen Gupta and S.C. Mukhopadhyay, “Sensors for Smart Home”, Chapter -7, of the book Human Behavior Recognition Technologies: Intelligent Applications for Monitoring and Security, edited by Hans Guesgen and Stephen Marsland, IGI Global, ISBN 978-1-4666-3683-5, page 130-156, 2013.
- M. Kulshreshtha and R. Mathur, “Dialect Accent Feature for Establishing Speaker Identity: A case study”, Springer Briefs in Electrical and Computer Engineering, 2012.
- Anindya Nag and Subhas Mukhopadhyay, Smart Home: Recognition of activities of elderly for 24/7; Coverage issues, Proceedings of the 2014 International Conference on Sensing Technology, Liverpool, UK, Sep. 2 to 4, 2014, pp. 480-489, ISSN 1178-5608, http://s2is.org/icst-2014/program.asp.
- M. Sigmund, “Statistical Analysis of Fundamental Frequency Based Features in Speech under Stress”. Information Technology and Control Journal, 42(3), pp. 286-291, 2013.
- S. A. Zahorian and H. Hu, “A spectral/temporal method for robust fundamental frequency tracking”. The Journal of the Acoustical Society of America, 123(6), pp. 4559-4571, 2008.
- Y.X. Lai, Y.M. Huang and S.C.Mukhopadhyay, Interconnecting Communication for Recognition and Automation services on Home Grid, Proceedings of IEEE I2MTC 2012 conference, IEEE Catalog number CFP12MT-CDR, ISBN 978-1-4577-1771-0, May 13-16, 2012, Graz, Austria, pp. 2346-2350.
- P. G. Deivapalan, M. Jha, R. Guttikonda and H. A. Murthy, “Donlabel: an automatic labeling tool for Indian languages.” Proceedings of Fourteenth National Conference on Communication (NCC), February 1-3, 2008, pp. 263-268.
- T. Quazi, S.C. Mukhopadhyay, N. Suryadevara and Y. M. Huang, Towards the Smart Sensors Based Human Emotion Recognition, Proceedings of IEEE I2MTC 2012 conference, IEEE Catalog number CFP12MT-CDR, ISBN 978-1-4577-1771-0, May 13-16, 2012, Graz, Austria, pp. 2365-2370.
- B. Yegnanarayana, “Artificial Neural Networks”. Prentice-Hall, New Delhi,2004,
- B. Yegnanarayana and S. P. Kishore, “AANN: an alternative to GMM for pattern recognition”, Neural Networks, 15(3), 459-469, 2002.
DOI: https://doi.org/10.21307/ijssis-2017-757 | Journal eISSN: 1178-5608
Language: English
Page range: 235 - 254
Submitted on: Nov 5, 2014
Accepted on: Jan 12, 2015
Published on: Mar 1, 2015
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
Related subjects:
© 2015 Shweta Sinha, Aruna Jain, S. S. Agrawal, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.