| Title | Performance Comparison of Support Vector Machine, K-Nearest-Neighbor, Artificial Neural Networks, and Recurrent Neural networks in Gender Recognition from Voice Signals |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Akdeniz, Fulya, Becerikli, Yaşar |
| Conference Name | 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) |
| Keywords | Artificial neural networks, audio signal, audio signal processing, biometric data, biometrics (access control), cyber physical systems, feature extraction, gender recognition, k-nearest neighborhood method, K-Nearest-Neighbor, mel frequency kepstrum coefficients, policy-based governance, pubcrawl, recurrent neural nets, Recurrent neural networks, Recurrent Neural Networks (RNN), Resiliency, support vector machine, Support vector machines, Voice Signal, voice signals |
| Abstract | Nowadays, biometric data is the most common used data in the field of security. Audio signals are one of these biometric data. Voice signals have used frequently in cases such as identification, banking systems, and forensic cases solution. The aim of this study is to determine the gender of voice signals. In the study, many different methods were used to determine the gender of voice signals. Firstly, Mel Frequency kepstrum coefficients were used to extract the feature from the audio signal. Subsequently, these attributes were classified with support vector machines, k-nearest neighborhood method and artificial neural networks. At the other stage of the study, it is aimed to determine gender from audio signals without using feature extraction method. For this, recurrent neural networks (RNN) was used. The performance analyzes of the methods used were made and the results were given. The best accuracy, precision, recall, f-score in the study has found to be 87.04%, 86.32%, 88.58%, 87.43% using K-Nearest-Neighbor algorithm. |
| DOI | 10.1109/ISMSIT.2019.8932818 |
| Citation Key | akdeniz_performance_2019 |
Performance Comparison of Support Vector Machine, K-Nearest-Neighbor, Artificial Neural Networks, and Recurrent Neural networks in Gender Recognition from Voice Signals