Dynamic Combination of Automatic Speech Recognition Systems by Driven Decoding

Combining automatic speech recognition (ASR) systems generally relies on the posterior merging of the outputs or on acoustic cross-adaptation. In this paper, we propose an integrated approach where outputs of secondary systems are integrated in the search algorithm of a primary one. In this driven d...

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Bibliographic Details
Published inIEEE transactions on audio, speech, and language processing Vol. 21; no. 6; pp. 1251 - 1260
Main Authors Lecouteux, B., Linares, G., Esteve, Y., Gravier, G.
Format Journal Article
LanguageEnglish
Published Piscataway, NJ IEEE 01.06.2013
Institute of Electrical and Electronics Engineers
Subjects
Acoustics
Adaptation models
Applied sciences
Automatic speech recognition
Coding, codes
Computer Science
Decoding
Exact sciences and technology
Hidden Markov models
Information, signal and communications theory
Multimedia
Pragmatics
Signal and communications theory
Signal processing
Speech
Speech processing
Speech recognition
system combination
Telecommunications and information theory
Performance evaluation
Error rate
Cable television
system combination
Decoding
Search algorithm
Automatic speech recognition
French
Audiovisual document
News
Speech recognition
Integrated system
Relative error
Robustness
Automatic recognition
Speech processing
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Summary:Combining automatic speech recognition (ASR) systems generally relies on the posterior merging of the outputs or on acoustic cross-adaptation. In this paper, we propose an integrated approach where outputs of secondary systems are integrated in the search algorithm of a primary one. In this driven decoding algorithm (DDA), the secondary systems are viewed as observation sources that should be evaluated and combined to others by a primary search algorithm. DDA is evaluated on a subset of the ESTER I corpus consisting of 4 hours of French radio broadcast news. Results demonstrate DDA significantly outperforms vote-based approaches: we obtain an improvement of 14.5% relative word error rate over the best single-systems, as opposed to the the 6.7% with a ROVER combination. An in-depth analysis of the DDA shows its ability to improve robustness (gains are greater in adverse conditions) and a relatively low dependency on the search algorithm. The application of DDA to both and beam-search-based decoder yields similar performances.
ISSN:1558-7916
1558-7924
DOI:10.1109/TASL.2013.2248716