Speech Rating System through Space Mapping

Predicting human behavior has been the subject of many research areas especially in machine learning. Due to its potential benefits, financially or otherwise, researchers have focused on modeling human behavior from recommending items in an online store to predicting the behavior of an entire ecosystem. In this paper, we make an attempt to predict human preference towards natural speech. The proposed approach makes use of extracted user features from the dataset using Singular Value Decomposition (SVD), features extracted from the wave signal using Mel-frequency cepstral coefficients (MFCC) and Radial Basis Function to map the two feature-spaces. The proposed approach was able to reach a Pearson Correlation Coefficient of 0.92 and a 0.258 MAE when compared to the original average scores. The main contribution of the presented work is the fact that mapping the signal-features (MFCC) into an intermediate feature space (SVD) is far more effective than mapping the signal features directly into the desired output. The proposed algorithm outperformed Support Vector Machines (SVM) in all measures, precisely by 88.14% in terms of correlation and by 48.62% in terms of error.