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http://dx.doi.org/10.9708/jksci.2022.27.10.059

Gender Classification of Speakers Using SVM  

Han, Sun-Hee (Dept. of Computer Science, Inha Technical College)
Cho, Kyu-Cheol (Dept. of Computer Science, Inha Technical College)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.10, 2022 , pp. 59-66 More about this Journal
Abstract
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%.
Keywords
Feature vectors; Voice; Classification; Mel frequency cepstral coefficient; Support vector machine;
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Times Cited By KSCI : 3  (Citation Analysis)
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