Language Recognition via Sparse Coding

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Language Recognition via Sparse Coding

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Title: Language Recognition via Sparse Coding
Author: Gwon, Youngjune Lee ORCID  0000-0002-2292-7320 ; Campbell, William M.; Sturim, Douglas E.; Kung, H. T.

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Citation: Gwon, Youngjune L., William M. Campbell, Douglas E. Sturim, and H. T. Kung. 2016. "Language Recognition via Sparse Coding." In Proc. Interspeech 2016, pp. 2920-2924.
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Abstract: Spoken language recognition requires a series of signal processing steps and learning algorithms to model distinguishing characteristics of different languages. In this paper, we present a sparse discriminative feature learning framework for language recognition. We use sparse coding, an unsupervised method, to compute efficient representations for spectral features from a speech utterance while learning basis vectors for language models. Differentiated from existing approaches in sparse representation classification, we introduce a maximum a posteriori (MAP) adaptation scheme based on online learning that further optimizes the discriminative quality of sparse-coded speech features. We empirically validate the effectiveness of our approach using the NIST LRE 2015 dataset.
Published Version: doi:10.21437/Interspeech.2016-881
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:33973840
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