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Authors: Thomas Plötz and Gernot A. Fink

Affiliation: University of Dortmund, Robotics Research Institute, Germany

Abstract: Major aspects of cognitive science are based on natural language processing utilizing automatic speech recognition (ASR) systems in scenarios of human-computer interaction. In order to improve the accuracy of related HMM-based ASR systems efficient approaches for un-supervised adaptation represent the methodology of choice. The recognition accuracy of speaker-specific recognition systems derived by on-line acoustic adaptation directly depends on the quality of the adaptation data actually used. It drops significantly if sample data out-of-scope (lexicon, acoustic conditions) of the original recognizer generating the necessary annotation is exploited without further analysis. In this paper we present an approach for fast and robust MLLR adaptation based on a rejection model which rapidly evaluates an alternative to existing confidence measures, so-called log-odd scores. These measures are computed as ratio of scores obtained from acoustic model evaluation to those produced by some r easonable background model. By means of log-odd scores threshold based detection and rejection of improper adaptation samples, i.e. out-of-domain data, is realized. By means of experimental evaluations on two challenging tasks we demonstrate the effectiveness of the proposed approach. (More)

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Paper citation in several formats:
Plötz, T. and A. Fink, G. (2007). An Efficient Method for Making Un-supervised Adaptation of HMM-based Speech Recognition Systems Robust Against Out-of-Domain Data. In Proceedings of the 4th International Workshop on Natural Language Processing and Cognitive Science (ICEIS 2007) - NLPCS; ISBN 978-972-8865-97-9, SciTePress, pages 109-118. DOI: 10.5220/0002416701090118

@conference{nlpcs07,
author={Thomas Plötz. and Gernot {A. Fink}.},
title={An Efficient Method for Making Un-supervised Adaptation of HMM-based Speech Recognition Systems Robust Against Out-of-Domain Data},
booktitle={Proceedings of the 4th International Workshop on Natural Language Processing and Cognitive Science (ICEIS 2007) - NLPCS},
year={2007},
pages={109-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002416701090118},
isbn={978-972-8865-97-9},
}

TY - CONF

JO - Proceedings of the 4th International Workshop on Natural Language Processing and Cognitive Science (ICEIS 2007) - NLPCS
TI - An Efficient Method for Making Un-supervised Adaptation of HMM-based Speech Recognition Systems Robust Against Out-of-Domain Data
SN - 978-972-8865-97-9
AU - Plötz, T.
AU - A. Fink, G.
PY - 2007
SP - 109
EP - 118
DO - 10.5220/0002416701090118
PB - SciTePress

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