Henderson, M and Gasic, M and Thomson, B and Tsiakoulis, P and Yu, K and Young, S (2012) Discriminative spoken language understanding using word confusion networks. 2012 IEEE Workshop on Spoken Language Technology, SLT 2012 - Proceedings. pp. 176-181.Full text not available from this repository.
Current commercial dialogue systems typically use hand-crafted grammars for Spoken Language Understanding (SLU) operating on the top one or two hypotheses output by the speech recogniser. These systems are expensive to develop and they suffer from significant degradation in performance when faced with recognition errors. This paper presents a robust method for SLU based on features extracted from the full posterior distribution of recognition hypotheses encoded in the form of word confusion networks. Following , the system uses SVM classifiers operating on n-gram features, trained on unaligned input/output pairs. Performance is evaluated on both an off-line corpus and on-line in a live user trial. It is shown that a statistical discriminative approach to SLU operating on the full posterior ASR output distribution can substantially improve performance both in terms of accuracy and overall dialogue reward. Furthermore, additional gains can be obtained by incorporating features from the previous system output. © 2012 IEEE.
|Divisions:||Div F > Machine Intelligence|
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|Date Deposited:||15 Dec 2015 13:00|
|Last Modified:||05 Feb 2016 21:43|