AbstractsEngineering

Classi cation of data has many applications, amongst others within the eld of speaker recognition. Speaker recognition is the part of speech processing concerned with the task of automatically identifying or verifying speakers using dierent characteristics of their voices. The main focus in speaker recognition is to nd methods that separate data, in order to dierentiate between dierent speakers. In this thesis, such a method is obtained by building a support vector machine, which has proved to be a very good tool for separating all kinds of data. The rst version of the support vector machine is used to separate linearly separable data using linear hyperplanes, and it is then modi ed to separate linearly non-separable data, by allowing some data points to be misclassi ed. Finally, the support vector machine is improved further, through a generalization to higher dimensional data and by the use of dierent kernels and thus higher order hyperplanes. The developed support vector machine is in the end used on a set of speaker recognition data. The separation of two speakers are not very satisfying, most likely due to the very limited set of data. However, the results are very good when the support vector machine is used on other, more complete, sets of data. ; Klassi cering av data har manga anvandningsomraden, bland annat inom rostigenkanning. Rostigenkanning ar en del av talmodellering som behandlar problemet med att kunna identi era talare och veri era en talares identitet med hjalp av karakteristiska drag hos dennes rost. Fokus ligger pa att hitta metoder som kan separera data, for att sedan kunna separera talare. I detta kandidatexamensarbete byggs, for detta syfte, en support vector machine som has visats vara ett bra satt att separera olika data. Den forsta versionen anvands pa data som ar linjart separerbart i tva dimensioner, sedan utvecklas den till att kunna separera data som inte ar linjart separerbart, genom att tillata vissa datapunkter att bli felklassi cerade. Slutligen modi eras denna support vector machine till att kunna separera data i hogre dimensioner, samt anvanda olika karnor for att ge separerande hyperplan av hogre ordning. Den fardiga versionen av denna support vector machine anvands till sist pa data for ett rostigenkanningsproblem. Resultatet av att separera tva talare var inte tillfredsstallande, dock skulle mer data fran olika talare ge ett battre resultat. Nar daretmot en annan, mer komplett, mangd av data anvands for att bygga denna support vector machine blir resultatet valdigt bra.