Bayesian compressive sensing for phonetic classification

Tara N. Sainath, Avishy Carmi, Dimitri Kanevsky, Bhuvana Ramabhadran

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

69 Scopus citations

Abstract

In this paper, we introduce a novel bayesian compressive sensing (CS) technique for phonetic classification. CS is often used to characterize a signal from a few support training examples, similar to k-nearest neighbor (kNN) and Support Vector Machines (SVMs). However, unlike SVMs and kNNs, CS allows the number of supports to be adapted to the specific signal being characterized. On the TIMIT phonetic classification task, we find that our CS method outperforms the SVM, kNN and Gaussian Mixture Model (GMM) methods. Our CS method achieves an accuracy of 80.01%, one of the best reported result in the literature to date.

Original languageEnglish
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages4370-4373
Number of pages4
DOIs
StatePublished - 8 Nov 2010
Externally publishedYes
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: 14 Mar 201019 Mar 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Country/TerritoryUnited States
CityDallas, TX
Period14/03/1019/03/10

Keywords

  • Compressive sensing
  • Pattern classification

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Bayesian compressive sensing for phonetic classification'. Together they form a unique fingerprint.

Cite this