Publication detail

Comparison of methods for language-dependent and language-independent query-by-example spoken term detection

TEJEDOR, J. FAPŠO, M. SZŐKE, I. ČERNOCKÝ, J. GRÉZL, F.

Original Title

Comparison of methods for language-dependent and language-independent query-by-example spoken term detection

Type

journal article - other

Language

English

Original Abstract

This article investigates query-by-example (QbE) spoken term detection (STD), in which the query is notentered as text, but selected in speech data or spoken. Two feature extractors based on neural networks(NN) are introduced: the first producing phone-state posteriors and the second making use of a compressiveNN layer. They are combined with three different QbE detectors: while the Gaussian mixture model/hiddenMarkov model (GMM/HMM) and dynamic time warping (DTW) both work on continuous feature vectors,the third one, based on weighted finite-state transducers (WFST), processes phone lattices.

Keywords

Experimentation, Query-by-example, DTW-based query-by-example, GMM/HMM-basedquery-by-example, WFST-based query-by-example, bottleneck features, keyword spotting

Authors

TEJEDOR, J.; FAPŠO, M.; SZŐKE, I.; ČERNOCKÝ, J.; GRÉZL, F.

RIV year

2012

Released

31. 8. 2012

Publisher

Association for Computing Machinery

Location

New York

ISBN

1046-8188

Periodical

ACM TRANSACTIONS ON INFORMATION SYSTEMS

Year of study

2012

Number

30

State

United States of America

Pages from

1

Pages to

34

Pages count

34

URL

http://dl.acm.org/citation.cfm?id=2328971&CFID=187707319&CFTOKEN=67886685

BibTex

@article{BUT97057,
  author="Javier {Tejedor} and Michal {Fapšo} and Igor {Szőke} and Jan {Černocký} and František {Grézl}",
  title="Comparison of methods for language-dependent and language-independent query-by-example spoken term detection",
  journal="ACM TRANSACTIONS ON INFORMATION SYSTEMS",
  year="2012",
  volume="2012",
  number="30",
  pages="1--34",
  doi="10.1145/2328967.2328971",
  issn="1046-8188",
  url="http://dl.acm.org/citation.cfm?id=2328971&CFID=187707319&CFTOKEN=67886685"
}