%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{Cernak_Idiap-RR-02-2013,
         author = {Cernak, Milos and Imseng, David and Bourlard, Herv{\'{e}}},
       projects = {Idiap, SNSF-MULTI},
          month = {1},
          title = {Robust triphone mapping for acoustic modeling},
           type = {Idiap-RR},
         number = {Idiap-RR-02-2013},
           year = {2013},
    institution = {Idiap},
       crossref = {Cernak_INTERSPEECH_2012},
       abstract = {In this paper we revisit the recently proposed triphone mapping as an alternative to decision tree state clustering. We generalize triphone mapping to Kullback-Leibler based hidden Markov models for acoustic modeling and propose a modified training procedure for the Gaussian mixture model based acoustic modeling. 
We compare the triphone mapping to decision tree state clustering on the Wall Street journal task as well as in the context of an under-resourced language by using Greek data from the SpeechDat(II) corpus. Experiments reveal that triphone mapping has the best overall performance and is robust against varying the acoustic modeling technique as well as variable amounts of training data.},
            pdf = {https://publications.idiap.ch/attachments/reports/2012/Cernak_Idiap-RR-02-2013.pdf}
}



crossreferenced publications: 
@INPROCEEDINGS{Cernak_INTERSPEECH_2012,
         author = {Cernak, Milos and Imseng, David and Bourlard, Herv{\'{e}}},
       keywords = {acoustic modeling, Kullback-Leibler divergence, speech recognition, triphone mapping},
       projects = {Idiap, SNSF-MULTI},
          month = sep,
          title = {Robust triphone mapping for acoustic modeling},
      booktitle = {Proceedings of Interspeech},
           year = {2012},
       location = {Portland, Oregon},
       abstract = {In this paper we revisit the recently proposed triphone mapping as an alternative to decision tree state clustering. We generalize triphone mapping to Kullback-Leibler based hidden Markov models for acoustic modeling and propose a modified training procedure for the Gaussian mixture model based acoustic modeling. 
We compare the triphone mapping to decision tree state clustering on the Wall Street journal task as well as in the context of an under-resourced language by using Greek data from the SpeechDat(II) corpus. Experiments reveal that triphone mapping has the best overall performance and is robust against varying the acoustic modeling technique as well as variable amounts of training data.},
            pdf = {https://publications.idiap.ch/attachments/papers/2012/Cernak_INTERSPEECH_2012.pdf}
}