Human Emotion detection from Audio clip
Supervector Dimension Reduction for Efficient Speaker Age Estimation Based on the ASS presents a novel dimension reduction method which aims to improve the accuracy and the efficiency of speaker’s age estimation systems based on speech signal. Two different
age estimation approaches were studied and implemented;
the first, age-group classification, and the second, precise age estimation using regression. These two approaches use the Gaussian mixture model (GMM) supervectors as features for a support vector machine (SVM) model. When a radial basis function (RBF) kernel is used, the accuracy is improved compared to using a linear kernel; however, the computation complexity is more sensitive to the feature dimension. Classic dimension reduction methods like principal component analysis (PCA) and linear discriminant analysis (LDA) tend to eliminate the relevant feature information and cannot always be applied without damaging the model’s accuracy.
Supervector Dimension Reduction for Efficient Speaker Age Estimation Based on the ASS presents a novel dimension reduction method which aims to improve the accuracy and the efficiency of speaker’s age estimation systems based on speech signal. Two different
age estimation approaches were studied and implemented;
the first, age-group classification, and the second, precise age estimation using regression. These two approaches use the Gaussian mixture model (GMM) supervectors as features for a support vector machine (SVM) model. When a radial basis function (RBF) kernel is used, the accuracy is improved compared to using a linear kernel; however, the computation complexity is more sensitive to the feature dimension. Classic dimension reduction methods like principal component analysis (PCA) and linear discriminant analysis (LDA) tend to eliminate the relevant feature information and cannot always be applied without damaging the model’s accuracy.
