dc.contributor.advisor | Svendsen, Torbjørn | nb_NO |
dc.contributor.author | Vindfallet, Vegar Enersen | nb_NO |
dc.date.accessioned | 2014-12-19T13:48:11Z | |
dc.date.accessioned | 2015-12-22T11:47:36Z | |
dc.date.available | 2014-12-19T13:48:11Z | |
dc.date.available | 2015-12-22T11:47:36Z | |
dc.date.created | 2012-11-20 | nb_NO |
dc.date.issued | 2012 | nb_NO |
dc.identifier | 570783 | nb_NO |
dc.identifier.uri | http://hdl.handle.net/11250/2370636 | |
dc.description.abstract | This thesis has taken a closer look at the implementation of the back-end of a language recognition system. The front-end of the system is a Universal Attribute Recognizer (UAR), which is used to detect phonetic characteristics in an utterance. When a speech signal is sent through the UAR, it is decoded into a sequence of attributes which is used to generate a vector of term-count. Vector Space Modeling (VSM) have been used for training the language classifiers in the back-end. The main principle of VSM is that term-count vectors from the same language will position themselves close to eachother when they are mapped into a vector space, and this property can be exploited for recognizing languages. The implemented back-end has trained vectors space classifiers for 12 different languages, and a NIST recognition task has been performed for evaluating the recognition rate of the system. The NIST task was a verification task and the system achived a equal error rate (EER) of $6.73 %$. Tools like Support Vector Machines (SVM) and Gaussian Mixture Models (GMM) have been used in the implementation of the back-end. Thus, are quite a few parameters which can be varied and tweaked, and different experiments were conducted to investigate how these parameters would affect EER of the language recognizer. As a part test the robustness of the system, the language recognizer were exposed to a so-called out-of-set language, which is a language that the system has not been trained to handle. The system showed a poor performance at rejecting these speech segments correctly. | nb_NO |
dc.language | eng | nb_NO |
dc.publisher | Institutt for elektronikk og telekommunikasjon | nb_NO |
dc.subject | ntnudaim:7993 | no_NO |
dc.title | Language Identification Based on Detection of Phonetic Characteristics | nb_NO |
dc.type | Master thesis | nb_NO |
dc.source.pagenumber | 38 | nb_NO |
dc.contributor.department | Norges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for elektronikk og telekommunikasjon | nb_NO |