Language Recognition via Sparse Coding
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CitationGwon, 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.
AbstractSpoken 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.
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