Language Identification based on Support Vector Machine using GMM Super vectors
Dr A.Nagesh, Professor in CSE at MGIT, Hyderabad, India
Manuscript received on March 15, 2020. | Revised Manuscript received on March 29, 2020. | Manuscript published on April 10, 2020. | PP: 1983-1986 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4805049620/2020©BEIESP | DOI: 10.35940/ijitee.F4805.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper proposes a novel approach that combines the power of generative Gaussian mixture models (GMM) and discriminative support vector machines (SVM). The main objective this paper is to incorporating the GMM super vectors based on SVM classifier for language identification (LID) task. The GMM based LID system to capture all the variations present in phonotactic constraints imposed by the language requires large amount of training data. The Gaussian mixture model (GMM)- universal background model (UBM) modeling require less amount of training data. In GMM-UBM LID system, a language model is created by maximum a posterior (MAP) adaptation of the means of the universal background model (UBM). Here the GMM super vectors are created by concatenating the means of the adapted mixture components from UBM. Then these super vectors are applied to a SVM for classification purpose. In this paper, the performance of GMM-UBM LID system based on SVM is compared with the conventional GMM LID system. Form the performance analysis it is found that GMM-UBM LID system based on SVM is performed well when compared to GMM based LID system.
Keywords: Language Identification, Gaussian Mixture Model, Support Vector Machine, Universal Background Model.
Scope of the Article: Machine Learning