Accession Number : AD1033827


Title :   Language Recognition via Sparse Coding


Descriptive Note : Technical Report


Corporate Author : MIT Lincoln Laboratory Lexington United States


Personal Author(s) : Gwon,Youngjune L ; Campbell,William M ; Sturim,Douglas ; Kung,H T


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1033827.pdf


Report Date : 08 Sep 2016


Pagination or Media Count : 4


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, we introduce a maximum a posteriori (MAP) adaptation scheme 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.


Descriptors :   speech recognition , language , machine learning , coding


Subject Categories : Voice Communications


Distribution Statement : APPROVED FOR PUBLIC RELEASE