Language Recognition via Sparse Coding

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.

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Document Details

Document Type
Technical Report
Publication Date
Sep 08, 2016
Accession Number
AD1033827

Entities

People

  • Douglas Sturim
  • H. T. Kung
  • William M. Campbell
  • Youngjune L. Gwon

Organizations

  • MIT Lincoln Laboratory

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Bayesian Networks
  • Computer Languages
  • Computer Programming
  • Computer Vision
  • Dimensionality Reduction
  • Information Science
  • Language
  • Machine Learning
  • Models
  • Neural Networks
  • Object Recognition
  • Probabilistic Models
  • Recognition
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Speech Processing/Speech Recognition.