%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{aradilla:rr08-14,
                      author = {Aradilla, Guillermo and Bourlard, Herv{\'{e}} and Magimai-Doss, Mathew},
                    projects = {Idiap},
                       title = {Using KL-based Acoustic Models in a Large Vocabulary Recognition Task},
                        type = {Idiap-RR},
                      number = {Idiap-RR-14-2008},
                        year = {2008},
                 institution = {IDIAP},
                    abstract = {Posterior probabilities of sub-word units have been shown to be an effective front-end for ASR. However, attempts to model this type of features either do not benefit from modeling context-dependent phonemes, or use an inefficient distribution to estimate the state likelihood. This paper presents a novel acoustic model for posterior features that overcomes these limitations. The proposed model can be seen as a HMM where the score associated with each state is the KL divergence between a distribution characterizing the state and the posterior features from the test utterance. This KL-based acoustic model establishes a framework where other models for posterior features such as hybrid HMM/MLP and discrete HMM can be seen as particular cases. Experiments on the WSJ database show that the KL-based acoustic model can significantly outperform these latter approaches. Moreover, the proposed model can obtain comparable results to complex systems, such as HMM/GMM, using significantly fewer parameters.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2008/aradilla-idiap-rr-08-14.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2008/aradilla-idiap-rr-08-14.ps.gz},
ipdmembership={speech},
}