Publication detail

Comparison of Keyword Spotting Approaches for Informal Continuous Speech

SZŐKE, I. SCHWARZ, P. MATĚJKA, P. BURGET, L. FAPŠO, M. KARAFIÁT, M. ČERNOCKÝ, J.

Original Title

Comparison of Keyword Spotting Approaches for Informal Continuous Speech

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

This paper describes several approaches to keyword spotting (KWS) for informal continuous speech. We compare acoustic keyword spotting, spotting in word lattices generated by large vocabulary continuous speech recognition and a hybrid approach making use of phoneme lattices generated by a phoneme recognizer. The systems are compared on carefully defined test data extracted from ICSI meeting database. The advantages and drawbacks of different approaches are discussed. The acoustic and phoneme-lattice based KWS are based on a phoneme recognizer making use of temporal-pattern (TRAP) feature extraction and posterior estimation using neural nets. We show its superiority over traditional HMM/GMM systems. A posterior probability transformation function is introduced for posterior based acoustic keyword spotting. We also propose a posterior masking algorithm to speed-up acoustic keyword spotting.

Keywords

comparison, keyword spotting, hidden Markov model, long temporal trajectory, phoneme recognizer

Authors

SZŐKE, I.; SCHWARZ, P.; MATĚJKA, P.; BURGET, L.; FAPŠO, M.; KARAFIÁT, M.; ČERNOCKÝ, J.

RIV year

2005

Released

20. 9. 2005

Location

Edinburgh

Pages from

1

Pages to

12

Pages count

12

URL

https://www.fit.vut.cz/research/publication/7887/

BibTex

@inproceedings{BUT18063,
  author="Igor {Szőke} and Petr {Schwarz} and Pavel {Matějka} and Lukáš {Burget} and Michal {Fapšo} and Martin {Karafiát} and Jan {Černocký}",
  title="Comparison of Keyword Spotting Approaches for Informal Continuous Speech",
  booktitle="2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms",
  year="2005",
  pages="1--12",
  address="Edinburgh",
  url="https://www.fit.vut.cz/research/publication/7887/"
}