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Master's Thesis from the year 2013 in the subject Computer Science - Miscellaneous, grade: A+, University of Manchester, language: English, abstract: The aim of this project was to create a gender identification system that can be§used to identify the gender of the speaker. In this dissertation I have explained the§signal processing background such as Fourier transforms and DCT etc. that was§needed to understand the underlying signal processing happening in digital devices.§Apart from that I also investigated the different classification techniques such as§Adaboost and Gaussian Mixture Models and different types of methods such as§Fusion method, acoustic methods and pitch methods used in gender identification.§From this perspective I have implemented 3 types of models (4 Models) that§are explained in the literature and introducing a new method for gender recognition§that uses SDC feature with pitch to identify the gender. All models were tested§and trained on the same amount of speech. The SDC and SDC fused model gave§satisfactory results on Voxforge dataset. Finally I tested the acoustic and fused§models on YouTube video which gave almost 90% accuracy. The results of my§implementations are shown in chapter 6.
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