Who is Speaking? Male or Female
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Sprache:Englisch
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Erscheinungsdatum
04.12.2013
Verlag
GRINSeitenzahl
79 (Printausgabe)
Dateigröße
1302 KB
Auflage
1. Auflage
Sprache
Englisch
EAN
9783656554363
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.
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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