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 not entered 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 compressive NN layer. They are combined with three different QbE detectors: while the Gaussian mixture model/hidden Markov 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-based query-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"
}