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 :

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