TAILIEUCHUNG - Báo cáo hóa học: " Research Article Using SVM as Back-End Classifier for Language Identification"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Using SVM as Back-End Classifier for Language Identification | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2008 Article ID 674859 6 pages doi 2008 674859 Research Article Using SVM as Back-End Classifier for Language Identification Hongbin Suo Ming Li Ping Lu and Yonghong Yan ThinkIT Speech Laboratory 109 DSP Building No. 21 Bei-Si-Huan-Xi Road Beijing 100190 China Correspondence should be addressed to Yonghong Yan yyan@ Received 31 January 2008 Accepted 29 September 2008 Recommended by Woon-Seng Gan Robust automatic language identification LID is a task of identifying the language from a short utterance spoken by an unknown speaker. One of the mainstream approaches named parallel phone recognition language modeling PPRLM has achieved a very good performance. The log-likelihood radio LLR algorithm has been proposed recently to normalize posteriori probabilities which are the outputs of back-end classifiers in PPRLM systems. Support vector machine SVM with radial basis function RBF kernel is adopted as the back-end classifier. But for the conventional SVM classifier the output is not probability. We use a pairwise posterior probability estimation PPPE algorithm to calibrate the output of each classifier. The proposed approaches are evaluated on the 2005 National Institute of Standards and Technology NIST . Language recognition evaluation databases and experiments show that the systems described in this paper produce comparable results to the existing arts. Copyright 2008 Hongbin Suo et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION Automatic spoken language identification without using deep knowledge of those languages is a challenging task. The variability of one spoken utterance can be incurred by its content speakers and environment. Normally the training corpus and test .

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