ARCHIVES
Research Article
Voice Based Gender Recognition Using Deep Learning
Dr. Sayyada Fahmeeda1
Mohamed Abdullah Ayan2
Mohamed Shamsuddin3
Aliya Amreen4
1Assistant Prof. Dept. of Computer Science and Engineering, PDA College Of Engineering, Kalaburagi. Karnataka, India. 234 Dept. of Computer Science and Engineering, PDA College Of Engineering, Kalaburagi. Karnataka, India.
Published Online: May-June 2022
Pages: 648-654
Cite this article
No DOIReferences
1. Alsulaiman, M., Ali, Z., & Muhammad, G. (2011, November). Gender classification with voice intensity. In 2011 UKSim 5th European
symposium on computer modeling and simulation (pp. 205–209). IEEE. https://doi.org/10.1109/EMS.2011.37
2. Archana, G. S., & Malleswari, M. (2015, April). Gender identification and performance analysis of speech signals. In 2015 Global
conference on communication technologies (GCCT) (pp. 483–489). IEEE. https://doi.org/10.1109/GCCT.2015.7342709
3. Chaudhary, S., & Sharma, D. K. (2018, October). Gender identification based on voice signal characteristics. In 2018 International
conference on advances in computing, communication control and networking (ICACCCN) (pp. 869–874). IEEE
https://doi.org/10.1109/ICACCCN.2018.8748676
4. Chen, G., Feng, X., Shue, Y. L., & Alwan, A. (2010, September). On using voice source measures in automatic gender classification of
children’s speech [Paper presentation]. Eleventh Annual Conference of the International Speech Communication Association, pp. 26–
30.
5. Djemili, R., Bourouba, H., & Korba, M. C. A. (2012, May). A speech signal based gender identification system using four classifiers. In
2012 International conference on multimedia computing and systems (pp. 184–187). IEEE.
https://doi.org/10.1109/ICMCS.2012.6320122
6. Ertam, F. (2019). An effective gender recognition approach using voice data via deeper LSTM networks. Applied Acoustics, 156, 351–
358. https://doi.org/10.1016/j.apacoust.2019.07.033
7. Gaikwad, S., Gawali, B., & Mehrotra, S. C. (2012). Gender identification using SVM with combination of MFCC. Advances in
Computational Research, 4(1), 69–73. Ghosal, A., & Dutta, S. (2014, February).
8. Automatic male-female voice discrimination. In 2014 International conference on issues and challenges in intelligent computing
techniques (ICICT) (pp. 731–735). IEEE. https://doi.org/10.1109/ICICICT.2014.6781371
9. Holzinger, A. (2019). Introduction to Machine learning & Knowledge Extraction (MAKE). Machine Learning and Knowledge Extraction,
1(1), 1–20. https://doi.org/10.3390/make1010001
10. Keyvanrad, M. A., & Homayounpour, M. M. (2010, May). Improvement on automatic speaker gender identification using classifier
fusion. In 2010 18th Iranian conference on electrical engineering (pp. 538–541). IEEE.
https://doi.org/10.1109/IRANIANCEE.2010.5507010
11. Kim, H. S. (n.d.). Linear predictive coding is all-pole resonance modeling. Center for Computer Research in Music and Acoustics,
Stanford University. Linear Interpolation. (2021, June 25). In Wikipedia. https://en.wikipedia.org/wiki/Linear_- interpolationLivieris, I.
12. E., Pintelas, E., & Pintelas, P. (2019). Gender recognition by voice using an improved self-labeled algorithm. Machine Learning and
Knowledge Extraction, 1(1), 492–503. https://doi.org/10. 3390/make1010030
13. Madhu, N. (2009). Note on measures for spectral flatness. Electronics Letters, 45(23), 1195–1196. https://doi.org/10.1049/el.2009.1977
14. Majkowski, A., Kołodziej, M., Pyszczak, J., Tarnowski, P., & Rak, R. J. (2019, September). Identification of gender based on speech
signal. In 2019 IEEE 20th International conference on computational problems of electrical engineering (CPEE) (pp. 1–4). IEEE.
https://doi.org/10.1109/CPEE47179. 2019.8949078
15. Pahwa, A., & Aggarwal, G. (2016). Speech feature extraction for gender recognition. International Journal of Image, Graphics and
Signal Processing, 8(9), 17. https://doi.org/10.5815/IJIGSP.2015. 09.03
symposium on computer modeling and simulation (pp. 205–209). IEEE. https://doi.org/10.1109/EMS.2011.37
2. Archana, G. S., & Malleswari, M. (2015, April). Gender identification and performance analysis of speech signals. In 2015 Global
conference on communication technologies (GCCT) (pp. 483–489). IEEE. https://doi.org/10.1109/GCCT.2015.7342709
3. Chaudhary, S., & Sharma, D. K. (2018, October). Gender identification based on voice signal characteristics. In 2018 International
conference on advances in computing, communication control and networking (ICACCCN) (pp. 869–874). IEEE
https://doi.org/10.1109/ICACCCN.2018.8748676
4. Chen, G., Feng, X., Shue, Y. L., & Alwan, A. (2010, September). On using voice source measures in automatic gender classification of
children’s speech [Paper presentation]. Eleventh Annual Conference of the International Speech Communication Association, pp. 26–
30.
5. Djemili, R., Bourouba, H., & Korba, M. C. A. (2012, May). A speech signal based gender identification system using four classifiers. In
2012 International conference on multimedia computing and systems (pp. 184–187). IEEE.
https://doi.org/10.1109/ICMCS.2012.6320122
6. Ertam, F. (2019). An effective gender recognition approach using voice data via deeper LSTM networks. Applied Acoustics, 156, 351–
358. https://doi.org/10.1016/j.apacoust.2019.07.033
7. Gaikwad, S., Gawali, B., & Mehrotra, S. C. (2012). Gender identification using SVM with combination of MFCC. Advances in
Computational Research, 4(1), 69–73. Ghosal, A., & Dutta, S. (2014, February).
8. Automatic male-female voice discrimination. In 2014 International conference on issues and challenges in intelligent computing
techniques (ICICT) (pp. 731–735). IEEE. https://doi.org/10.1109/ICICICT.2014.6781371
9. Holzinger, A. (2019). Introduction to Machine learning & Knowledge Extraction (MAKE). Machine Learning and Knowledge Extraction,
1(1), 1–20. https://doi.org/10.3390/make1010001
10. Keyvanrad, M. A., & Homayounpour, M. M. (2010, May). Improvement on automatic speaker gender identification using classifier
fusion. In 2010 18th Iranian conference on electrical engineering (pp. 538–541). IEEE.
https://doi.org/10.1109/IRANIANCEE.2010.5507010
11. Kim, H. S. (n.d.). Linear predictive coding is all-pole resonance modeling. Center for Computer Research in Music and Acoustics,
Stanford University. Linear Interpolation. (2021, June 25). In Wikipedia. https://en.wikipedia.org/wiki/Linear_- interpolationLivieris, I.
12. E., Pintelas, E., & Pintelas, P. (2019). Gender recognition by voice using an improved self-labeled algorithm. Machine Learning and
Knowledge Extraction, 1(1), 492–503. https://doi.org/10. 3390/make1010030
13. Madhu, N. (2009). Note on measures for spectral flatness. Electronics Letters, 45(23), 1195–1196. https://doi.org/10.1049/el.2009.1977
14. Majkowski, A., Kołodziej, M., Pyszczak, J., Tarnowski, P., & Rak, R. J. (2019, September). Identification of gender based on speech
signal. In 2019 IEEE 20th International conference on computational problems of electrical engineering (CPEE) (pp. 1–4). IEEE.
https://doi.org/10.1109/CPEE47179. 2019.8949078
15. Pahwa, A., & Aggarwal, G. (2016). Speech feature extraction for gender recognition. International Journal of Image, Graphics and
Signal Processing, 8(9), 17. https://doi.org/10.5815/IJIGSP.2015. 09.03
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