This research conducted a study classifying gender of speakers by analyzing feature vectors extracted from the voice data. The study provides convenience in automatically recognizing gender of customers without manual classification process when they request any service via voice such as phone call.
Furthermore, it is significant that this study can analyze frequently requested services for each gender after gender classification using a learning model and offer customized recommendation services according to the analysis. Based on the voice data of males and females excluding blank spaces, the study extracts feature vectors from each data using MFCC(Mel Frequency Cepstral Coefficient) and utilizes SVM(Support Vector Machine) models to conduct machine learning. As a result of gender classification of voice data using a learning model, the gender recognition rate was 94%.
@article{ART002889738}, author={Sun-Hee Han and Kyu-Cheol Cho}, title={Gender Classification of Speakers Using SVM}, journal={Journal of The Korea Society of Computer and Information}, issn={1598-849X}, year={2022}, volume={27}, number={10}, pages={59-66}, doi={10.9708/jksci.2022.27.10.059}
TY - JOUR AU - Sun-Hee Han AU - Kyu-Cheol Cho TI - Gender Classification of Speakers Using SVM JO - Journal of The Korea Society of Computer and Information PY - 2022 VL - 27 IS - 10 PB - The Korean Society Of Computer And Information SP - 59 EP - 66 SN - 1598-849X AB - This research conducted a study classifying gender of speakers by analyzing feature vectors extracted from the voice data. The study provides convenience in automatically recognizing gender of customers without manual classification process when they request any service via voice such as phone call.
Furthermore, it is significant that this study can analyze frequently requested services for each gender after gender classification using a learning model and offer customized recommendation services according to the analysis. Based on the voice data of males and females excluding blank spaces, the study extracts feature vectors from each data using MFCC(Mel Frequency Cepstral Coefficient) and utilizes SVM(Support Vector Machine) models to conduct machine learning. As a result of gender classification of voice data using a learning model, the gender recognition rate was 94%. KW - Feature vectors;Voice;Classification;Mel frequency cepstral coefficient;Support vector machine DO - 10.9708/jksci.2022.27.10.059 ER -
Sun-Hee Han and Kyu-Cheol Cho. (2022). Gender Classification of Speakers Using SVM. Journal of The Korea Society of Computer and Information, 27(10), 59-66.
Sun-Hee Han and Kyu-Cheol Cho. 2022, "Gender Classification of Speakers Using SVM", Journal of The Korea Society of Computer and Information, vol.27, no.10 pp.59-66. Available from: doi:10.9708/jksci.2022.27.10.059
Sun-Hee Han, Kyu-Cheol Cho "Gender Classification of Speakers Using SVM" Journal of The Korea Society of Computer and Information 27.10 pp.59-66 (2022) : 59.
Sun-Hee Han, Kyu-Cheol Cho. Gender Classification of Speakers Using SVM. 2022; 27(10), 59-66. Available from: doi:10.9708/jksci.2022.27.10.059
Sun-Hee Han and Kyu-Cheol Cho. "Gender Classification of Speakers Using SVM" Journal of The Korea Society of Computer and Information 27, no.10 (2022) : 59-66.doi: 10.9708/jksci.2022.27.10.059
Sun-Hee Han; Kyu-Cheol Cho. Gender Classification of Speakers Using SVM. Journal of The Korea Society of Computer and Information, 27(10), 59-66. doi: 10.9708/jksci.2022.27.10.059
Sun-Hee Han; Kyu-Cheol Cho. Gender Classification of Speakers Using SVM. Journal of The Korea Society of Computer and Information. 2022; 27(10) 59-66. doi: 10.9708/jksci.2022.27.10.059
Sun-Hee Han, Kyu-Cheol Cho. Gender Classification of Speakers Using SVM. 2022; 27(10), 59-66. Available from: doi:10.9708/jksci.2022.27.10.059
Sun-Hee Han and Kyu-Cheol Cho. "Gender Classification of Speakers Using SVM" Journal of The Korea Society of Computer and Information 27, no.10 (2022) : 59-66.doi: 10.9708/jksci.2022.27.10.059