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Language Identification Based on Detection of Phonetic Characteristics
Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, Department of Electronics and Telecommunications.
2012 (English)MasteroppgaveStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
Institutt for elektronikk og telekommunikasjon , 2012. , 38 p.
Keyword [no]
ntnudaim:7993, MTEL elektronikk, Multimedia-signalbehandling
URN: urn:nbn:no:ntnu:diva-19506Local ID: ntnudaim:7993OAI: diva2:570783
Available from: 2012-11-20 Created: 2012-11-20

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