Yıl 2020,
Cilt: 8 Sayı: 4, 232 - 235, 31.12.2020
Yavuz Selim Taşpınar
,
Mücahid Mustafa Sarıtaş
,
İlkay Çınar
,
Murat Koklu
Kaynakça
- Amodei, D., et al. Deep speech 2: End-to-end speech recognition in english and mandarin. in International conference on machine learning. 2016.
- Chen, C., et al., A bilevel framework for joint optimization of session compensation and classification for speaker identification. Digital Signal Processing, 2019. 89: p. 104-115.
- Black, M., et al. Automatic classification of married couples' behavior using audio features. in Eleventh annual conference of the international speech communication association. 2010.
- Metze, F., et al. Comparison of four approaches to age and gender recognition for telephone applications. in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07. 2007. IEEE.
- Konig, Y., N. Morgan, and C. Chandra, GDNN: a gender-dependent neural network for continuous speech recognition. 1991: International Computer Science Institute.
- RAMADHAN, M.M., et al., Parameter Tuning in Random Forest Based on Grid Search Method for Gender Classification Based on Voice Frequency. DEStech Transactions on Computer Science and Engineering, 2017(cece).
- Jain, A. and V. Kanhangad, Gender recognition in smartphones using touchscreen gestures. Pattern Recognition Letters, 2019. 125: p. 604-611.
- Markitantov, M. and O. Verkholyak. Automatic Recognition of Speaker Age and Gender Based on Deep Neural Networks. in International Conference on Speech and Computer. 2019. Springer.
- Zvarevashe, K. and O.O. Olugbara. Gender voice recognition using random forest recursive feature elimination with gradient boosting machines. in 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD). 2018. IEEE.
- Gupta, P., S. Goel, and A. Purwar. A stacked technique for gender recognition through voice. in 2018 Eleventh International Conference on Contemporary Computing (IC3). 2018. IEEE.
- Sharma, G. and S. Mala. Framework for gender recognition using voice. in 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). 2020. IEEE.
- Buyukyilmaz, M. and A.O. Cibikdiken. Voice gender recognition using deep learning. in 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016). 2016. Atlantis Press.
- Dataset. Dataset. 08.07.2020]; Available from: https://raw.githubusercontent.com/primaryobjects/voice-gender/master/voice.csv.
- Araya‐Salas, M. and G. Smith‐Vidaurre, warbleR: an R package to streamline analysis of animal acoustic signals. Methods in Ecology and Evolution, 2017. 8(2): p. 184-191.
- Ertam, F., An effective gender recognition approach using voice data via deeper LSTM networks. Applied Acoustics, 2019. 156: p. 351-358.
- Akçayol, M., Bir anahtarlamalı relüktans motorun sinirsel-bulanık denetimi. 2001, Doktora Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
Gender Determination Using Voice Data
Yıl 2020,
Cilt: 8 Sayı: 4, 232 - 235, 31.12.2020
Yavuz Selim Taşpınar
,
Mücahid Mustafa Sarıtaş
,
İlkay Çınar
,
Murat Koklu
Öz
The rapid advancement of today's technologies, it is tried to facilitate whichever system will be used by using voice features such as person recognition and speech recognition by making use of the voices of the users. Organizations serving in these systems need less manpower and facilitate the operation by helping users faster. The decision-making process using sound features is a very challenging process. With gender recognition, which is one of these steps, it is possible to address the user by gender. In this study, it is aimed to define the genders according to the voices in terms of both forensic informatics and the rapid and accurate progress of the processes. In this study, 3168 male and female voice samples were taken as a dataset. Sound samples were first analyzed by acoustic analysis in R using seewave and tuneR packages. Artificial neural networks were used in the classification stage. In order to increase the classification accuracy, the dataset was divided into 10 parts and each part was excluded from training for testing and used for retesting. Average classification success was found by taking the arithmetic mean of the results. In the classification made with artificial neural networks, male and female voices could be distinguished from each other with a success of 97.9%.
Kaynakça
- Amodei, D., et al. Deep speech 2: End-to-end speech recognition in english and mandarin. in International conference on machine learning. 2016.
- Chen, C., et al., A bilevel framework for joint optimization of session compensation and classification for speaker identification. Digital Signal Processing, 2019. 89: p. 104-115.
- Black, M., et al. Automatic classification of married couples' behavior using audio features. in Eleventh annual conference of the international speech communication association. 2010.
- Metze, F., et al. Comparison of four approaches to age and gender recognition for telephone applications. in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP'07. 2007. IEEE.
- Konig, Y., N. Morgan, and C. Chandra, GDNN: a gender-dependent neural network for continuous speech recognition. 1991: International Computer Science Institute.
- RAMADHAN, M.M., et al., Parameter Tuning in Random Forest Based on Grid Search Method for Gender Classification Based on Voice Frequency. DEStech Transactions on Computer Science and Engineering, 2017(cece).
- Jain, A. and V. Kanhangad, Gender recognition in smartphones using touchscreen gestures. Pattern Recognition Letters, 2019. 125: p. 604-611.
- Markitantov, M. and O. Verkholyak. Automatic Recognition of Speaker Age and Gender Based on Deep Neural Networks. in International Conference on Speech and Computer. 2019. Springer.
- Zvarevashe, K. and O.O. Olugbara. Gender voice recognition using random forest recursive feature elimination with gradient boosting machines. in 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD). 2018. IEEE.
- Gupta, P., S. Goel, and A. Purwar. A stacked technique for gender recognition through voice. in 2018 Eleventh International Conference on Contemporary Computing (IC3). 2018. IEEE.
- Sharma, G. and S. Mala. Framework for gender recognition using voice. in 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). 2020. IEEE.
- Buyukyilmaz, M. and A.O. Cibikdiken. Voice gender recognition using deep learning. in 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016). 2016. Atlantis Press.
- Dataset. Dataset. 08.07.2020]; Available from: https://raw.githubusercontent.com/primaryobjects/voice-gender/master/voice.csv.
- Araya‐Salas, M. and G. Smith‐Vidaurre, warbleR: an R package to streamline analysis of animal acoustic signals. Methods in Ecology and Evolution, 2017. 8(2): p. 184-191.
- Ertam, F., An effective gender recognition approach using voice data via deeper LSTM networks. Applied Acoustics, 2019. 156: p. 351-358.
- Akçayol, M., Bir anahtarlamalı relüktans motorun sinirsel-bulanık denetimi. 2001, Doktora Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.