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Speech based Gender Identification using Feed Forward Neural Networks

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IJCA Proceedings on National Conference on Recent Trends in Information Technology
© 2016 by IJCA Journal
NCIT 2015 - Number 1
Year of Publication: 2016
Authors:
Seema Khanum
Marpe Sora

Seema Khanum and Marpe Sora. Article: Speech based Gender Identification using Feed Forward Neural Networks. IJCA Proceedings on National Conference on Recent Trends in Information Technology NCIT 2015(1):5-8, January 2016. Full text available. BibTeX

@article{key:article,
	author = {Seema Khanum and Marpe Sora},
	title = {Article: Speech based Gender Identification using Feed Forward Neural Networks},
	journal = {IJCA Proceedings on National Conference on Recent Trends in Information Technology},
	year = {2016},
	volume = {NCIT 2015},
	number = {1},
	pages = {5-8},
	month = {January},
	note = {Full text available}
}

Abstract

This paper proposes an efficient method of gender identification based on the speaker's voice in a noisy environment. MFCC was used to extract features from the speech sample taken from a noisy speech database; these features are then used to train Artificial Neural Network architecture to classify two different genders (Male and Female). The test result shows that the new proposed ANN architecture can analyze and learn better and faster. The advantage of proposed method is a result of decreasing the number of segments by grouping similar segments in training data using a clustering technique namely fuzzy c means clustering.

References

  • Madhavi S. Pednekar, KavitaTiware and SachinBhagwat, "Gender Distinction Using Short Segments of Speech Signal", IJCSNS International Journal of Computer Science and Network Security, VOL. 8, No. 10, October 2008.
  • Czyzewski, Andrzej; Dziubinski, Marek; Kotus, Jozef; Pawlik, Arkadiusz; Rypulak, Andrzej; Szwoch, Grzegorz, "Multitask Noisy Speech Enhancement System", 26th International Conference: Audio Forensics in the Digital Age (July 2005).
  • Mukherjee, R, Islam, T. Sankar, R, "Text dependent speaker recognition using shifted MFCC", Southeastcon, 2013 Proceedings of IEEE, pp. 1 – 4
  • L. Rabiner, B. H. Juang, "Fundamentals of Speech Recognition", Pearson Education, 2009.
  • Madhavi S. Pednekar, KavitaTiware and SachinBhagwat, "Continuous Speech Recognition forMarathi Language Using Statistical Method", IEEE International Conference on „Computer Vision andInformation Technology, Advances and Applications?, ACVIT-09, December 2009, pp. 810-816.
  • Mohammad Reza Homaeinezhad, EhsanTavakkoli, Ali Ghaffari, "Discrete Wavelet based Fuzzy Network Architecture for ECG Rhythm Type Recognition: Feature Extraction and Clustering Oriented Tuning of Fuzzy Inference System" International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 4, No. 3, September, 2011.
  • IEEE Subcommittee (1969). "IEEE Recommended Practice for Speech Quality Measurements. IEEE Trans. Audio and Electroacoustics", AU-17(3), 225-246.
  • Hu, Y. and Loizou, P. (2007). "Subjective evaluation and comparison of speech enhancement algorithms," Speech Communication, 49, 588-601.