Personal information from Bangla speech signal using MFCC and GMM

Abstract

Our system extracts personal information from bangla speech. Dataset that was used consists real-life voice inputs from di erent age and gender groups. A set of Bengali speech samples from YouTube were used as input dataset. This system is based on basic machine learning algorithms. Mel frequency cepstral coe cient was used to train and construct this system. While calculating gender and age detection part, we will be using GMM to calculate the nal scores on the samples having the MFCCs of the extracted speech samples. GMM model basically congregates some subsets among the whole set based on probability. Along with the gender determination process, age detection process will also be simulated using fundamental frequency of speech. Python is the programming language used to write the coding. Our system was successful in giving 88% accuracy for gender recognition and 75% accuracy for age detection.