Title :
SVM-UBM Based Automatic Language Identification Using a Vowel-guided Segmentation
Author :
Peng, Tianqiang ; Zhang, Wenlin ; Li, Bicheng
Author_Institution :
ZhengZhou Inf. Sci. & Technol. Inst., Zhengzhou
Abstract :
As powerful theoretical and computational tools, support vector machines (SVMs) have been widely used in pattern classification of many areas. A key issue of applying SVMs to language identification of speech signals is to find a SVM kernel that compares a sequence of feature vectors with others efficiently. In this paper, we introduce a sequence kernel used in language identification, and develop a Gaussian Mixture Model to do the sequence mapping task, which maps a variable length sequence of vectors to a fixed dimensional space. Experiment results demonstrate that the new system not only yields performance superior to those of a GMM classifier but also outperforms the system using Generalized Linear Discriminant Sequence (GLDS) Kernel.
Keywords :
Gaussian processes; natural language processing; speech recognition; statistical analysis; support vector machines; Gaussian mixture model; SVM-UBM; automatic language identification; generalized linear discriminant sequence kernel; sequence mapping task; support vector machines; variable length sequence; vowel-guided segmentation; Application software; Information science; Kernel; Natural languages; Pattern classification; Signal processing; Speech; Support vector machine classification; Support vector machines; Training data;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.701