NDLI: Rapid Language Identification

Content Provider	IEEE Xplore Digital Library
Author	Van Segbroeck, M. Travadi, R. Narayanan, S.S.
Copyright Year	2014
Abstract	A critical challenge to automatic language identification (LID) is achieving accurate performance with the shortest possible speech segment in a rapid fashion. The accuracy to correctly identify the spoken language is highly sensitive to the duration of speech and is bounded by the amount of information available. The proposed approach for rapid language identification transforms the utterances to a low dimensional i-vector representation upon which language classification methods are applied. In order to meet the challenges involved in rapidly making reliable decisions about the spoken language, a highly accurate and computationally efficient framework of i-vector extraction is proposed. The LID framework integrates the approach of universal background model (UBM) fused total variability modeling. UBM-fused modeling yields the estimation of a more discriminant, single i-vector space. This way, it is also a computationally more efficient alternative than system level fusion. A further reduction in equal error rate is achieved by training the i-vector model on long duration speech utterances and by the deployment of a robust feature extraction scheme that aims to capture the relevant language cues under various acoustic conditions. Evaluation results on the DARPA RATS data corpus suggest the potential of performing successful automated language identification at the level of one second of speech or even shorter duration.
Starting Page	1118
Ending Page	1129
Page Count	12
File Size	1367733
File Format	PDF
ISSN	23299290
Volume Number	23
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Acoustics Feature extraction Robustness Training Computational modeling Speech processing universal background model (UBM) fusion I-vector noise robustness rapid language identification short-duration speech total variability modeling
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Signal Processing Instrumentation Speech and Hearing Electrical and Electronic Engineering Computational Mathematics Media Technology

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