Phoneme based acoustics keyword spotting in informal continuous speech

Authors:
Igor Szöke;Petr Schwarz;Pavel Matějka;Lukáš Burget;Martin Karafiát;Jan Černocký
Affiliations:
Faculty of Information Technology, Brno University of Technology, Czech Republic;Faculty of Information Technology, Brno University of Technology, Czech Republic;Faculty of Information Technology, Brno University of Technology, Czech Republic;Faculty of Information Technology, Brno University of Technology, Czech Republic;Faculty of Information Technology, Brno University of Technology, Czech Republic;Faculty of Information Technology, Brno University of Technology, Czech Republic
Venue:
TSD'05 Proceedings of the 8th international conference on Text, Speech and Dialogue
Year:
2005

Citing 1
Cited 2

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Abstract

This paper describes several ways of acoustic keywords spotting (KWS), based on Gaussian mixture model (GMM) hidden Markov models (HMM) and phoneme posterior probabilities from FeatureNet. Context-independent and dependent phoneme models are used in the GMM/HMM system. The systems were trained and evaluated on informal continuous speech. We used different complexities of KWS recognition network and different types of phoneme models. We study the impact of these parameters on the accuracy and computational complexity, an conclude that phoneme posteriors outperform conventional GMM/HMM system.