Classification of speech signal based on gender: a hybrid approach using neuro-fuzzy systems-Reference-Cited by-同舟云学术

Classification of speech signal based on gender: a hybrid approach using neuro-fuzzy systems

Published:2011-10-12 Issue:4 Volume:14 Page:377-391
ISSN:1381-2416
Container-title:International Journal of Speech Technology
language:en
Short-container-title:Int J Speech Technol

Author:

Gomathy M.,Meena K.,Subramaniam K. R.

Publisher

Springer Science and Business Media LLC

Subject

Computer Vision and Pattern Recognition,Linguistics and Language,Human-Computer Interaction,Language and Linguistics,Software

Link

http://link.springer.com/content/pdf/10.1007/s10772-011-9118-0.pdf

Reference23 articles.

1. Devi, T. M., Kasthuri, N., & Natarajan, A. M. (2010). Performance comparison of noise classification using intelligent networks. International Journal of Electronics Engineering, 2(1), 49–54.

2. Faúndez-Zanuy, M., McLaughlin, S., Esposito, A., Hussain, A., Schoentgen, J., Kubin, G., Kleijn, W. B., & Maragos, P. (2002). Non-linear speech processing: overview and applications. Control & Intelligent Systems, 30(1), 1–10.

3. Gomathy, M., Meena, K., & Subramaniam, K. R. (2011, to be published). Gender grouping in speech recognition using statistical metrics of pitch strength. EJSR J.

4. Gudi, A. B., & Nagaraj, H. C. (2009). Optimal curve fitting of speech signal for disabled children. International Journal of Computer science & Information Technology (IJCSIT), 1(2), 99–107.

5. Gudi, A. B., Shreedhar, H. K., & Nagaraj, H. C. (2010). Signal processing techniques to estimate the speech disability in children. IACSIT International Journal of Engineering and Technology, 2(2), 169–176.

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