Voice based gender classification using machine learning

14th International Conference on Science, Engineering and Technology

自然科学;工业技术

Raahul, A.^1 ; Sapthagiri, R.^1 ; Pankaj, K.^1 ; Vijayarajan, V.^1

School of Computer Science and Engineering, VIT University, Vellore

632014, India^1

关键词: Classification and regression tree;    Comparative modeling;    Gender classification;    Gender identification;    K nearest neighbours (k-NN);    Linear discriminant analyses (LDA);    Misclassification rates;    Performance metrics;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042083/pdf
DOI  :  10.1088/1757-899X/263/4/042083

来源: IOP

【 摘 要 】

Gender identification is one of the major problem speech analysis today. Tracing the gender from acoustic data i.e., pitch, median, frequency etc. Machine learning gives promising results for classification problem in all the research domains. There are several performance metrics to evaluate algorithms of an area. Our Comparative model algorithm for evaluating 5 different machine learning algorithms based on eight different metrics in gender classification from acoustic data. Agenda is to identify gender, with five different algorithms: Linear Discriminant Analysis (LDA), K-Nearest Neighbour (KNN), Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) on basis of eight different metrics. The main parameter in evaluating any algorithms is its performance. Misclassification rate must be less in classification problems, which says that the accuracy rate must be high. Location and gender of the person have become very crucial in economic markets in the form of AdSense. Here with this comparative model algorithm, we are trying to assess the different ML algorithms and find the best fit for gender classification of acoustic data.