%Aigaion2 BibTeX export from Idiap Publications
%Saturday 21 December 2024 05:03:15 PM

@TECHREPORT{Imseng_Idiap-RR-15-2012,
         author = {Imseng, David and Bourlard, Herv{\'{e}} and Garner, Philip N.},
       projects = {Idiap, SNSF-MULTI, IM2},
          month = {6},
          title = {Boosting under-resourced speech recognizers by exploiting out of language data - Case study on Afrikaans},
           type = {Idiap-RR},
         number = {Idiap-RR-15-2012},
           year = {2012},
    institution = {Idiap},
       crossref = {Imseng_SLTU_2012},
       abstract = {Under-resourced speech recognizers may benefit from data in languages other than the target language. In this paper, we boost the performance of an Afrikaans speech recognizer by using already available data from other languages. To successfully exploit available multilingual resources, we use posterior features, estimated by multilayer perceptrons that are trained on similar languages. For two different acoustic modeling techniques, Tandem and Kullback-Leibler divergence based HMMs, the proposed multilingual system yields more than 10\% relative improvement compared to the corresponding monolingual systems only trained on Afrikaans.},
            pdf = {https://publications.idiap.ch/attachments/reports/2012/Imseng_Idiap-RR-15-2012.pdf}
}



crossreferenced publications: 
@INPROCEEDINGS{Imseng_SLTU_2012,
         author = {Imseng, David and Bourlard, Herv{\'{e}} and Garner, Philip N.},
       keywords = {Afrikaans, multilingual speech recognition, Posterior features, under-resourced languages},
       projects = {Idiap, IM2, SNSF-MULTI},
          month = may,
          title = {Boosting under-resourced speech recognizers by exploiting out of language data - Case study on Afrikaans},
      booktitle = {Proceedings of the 3rd International Workshop on Spoken Languages Technologies for Under-resourced Languages},
           year = {2012},
          pages = {60--67},
       location = {Cape Town},
       abstract = {Under-resourced speech recognizers may benefit from data in languages other than the target language. In this paper, we boost  the performance of an Afrikaans speech recognizer by using already available data from other languages. To successfully exploit available multilingual resources, we use posterior features, estimated by multilayer perceptrons that are trained on similar languages. For two different acoustic modeling techniques, Tandem and Kullback-Leibler divergence based HMMs, the proposed multilingual system yields more than 10\% relative improvement compared to the corresponding monolingual systems only trained on Afrikaans.},
            pdf = {https://publications.idiap.ch/attachments/papers/2012/Imseng_SLTU_2012.pdf}
}