Information Theoretic Based Segments for Language Identification
TSD '99 Proceedings of the Second International Workshop on Text, Speech and Dialogue
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
Segmented and unsegmented dialogue-act annotation with statistical dialogue models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
| Hi-index | 0.00 |
In this paper we present a new approach for estimating the interpolation parameters of language models (LM) which are used as classifiers. With the classical maximum likelihood (ML) estimation theoretically one needs to have a huge amount of data and the fundamental density assumption has to be correct. Usually one of these conditions is violated, so different optimization techniques like maximum mutual information (MMI) and minimum classification error (MCE) can be used instead, where the interpolation parameters are not optimized on their own but in consideration of all models together. In this paper we present how MCE and MMI techniques can be applied to two different kind of interpolation strategies: the linear interpolation, which is the standard interpolation method and the rational interpolation. We compare ML, MCE and MMI on the German part of the Verbmobil corpus, where we get a reduction of 3% of classification error when discriminating between 18 dialog act classes.