%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{aradilla:rr06-60,
                      author = {Aradilla, Guillermo and Vepa, Jithendra and Bourlard, Herv{\'{e}}},
                    projects = {Idiap},
                       title = {An Acoustic Model Based on Kullback-Leibler Divergence for Posterior Features},
                        type = {Idiap-RR},
                      number = {Idiap-RR-60-2006},
                        year = {2006},
                 institution = {IDIAP},
                    abstract = {This paper investigates the use of features based on posterior probabilities of subword units such as phonemes. These features are typically transformed when used as inputs for a hidden Markov model with mixture of Gaussians as emission distribution (HMM/GMM). In this work, we introduce a novel acoustic model that avoids the Gaussian assumption and directly uses posterior features without any transformation. This model is described by a finite state machine where each state is characterized by a target distribution and the cost function associated to each state is given by the Kullback-Leibler (KL) divergence between its target distribution and the posterior features. Furthermore, hybrid HMM/ANN system can be seen as a particular case of this KL-based model where state target distributions are predefined. A training method is also presented that minimizes the KL-divergence between the state target distributions and the posteriors features.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2006/aradilla-idiap-rr-06-60.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2006/aradilla-idiap-rr-06-60.ps.gz},
ipdmembership={speech},
}