%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Motlicek_ICASSP2013-2_2013,
         author = {Motlicek, Petr and Garner, Philip N. and Kim, Namhoon and Cho, Jeongmi},
       projects = {Idiap, SAMSUNG},
          month = may,
          title = {ACCENT ADAPTATION USING SUBSPACE GAUSSIAN MIXTURE MODELS},
      booktitle = {The 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
           year = {2013},
          pages = {7170-7174},
       location = {Vancouver, BC, Canada},
   organization = {IEEE},
           issn = {1520-6149},
            doi = {10.1109/ICASSP.2013.6639054},
       crossref = {Motlicek_Idiap-RR-38-2013},
       abstract = {This paper investigates employment of Subspace Gaussian Mixture Models (SGMMs) for acoustic model adaptation towards different accents for English speech recognition. The SGMMs comprise globally-shared and state-specific parameters which can efficiently be employed for various kinds of acoustic parameter tying. Research results indicate that well-defined sharing of acoustic model parameters in SGMMs can significantly outperform adapted systems based on conventional HMM/GMMs. Furthermore, SGMMs rapidly achieve target acoustic models with small amounts of data. Experiments performed with US and UK English versions of the
Wall Street Journal (WSJ) corpora indicate that SGMMs lead to approximately 20\% and 8\% relative improvements with respect to speaker-independent and speaker-adapted acoustic models respectively over conventional HMM/GMMs. Finally, we demonstrate that SGMMs adapted only with 1.5 hours can reach performance of HMM/GMMs trained with 18 hours.},
            pdf = {https://publications.idiap.ch/attachments/papers/2013/Motlicek_ICASSP2013-2_2013.pdf}
}



crossreferenced publications: 
@TECHREPORT{Motlicek_Idiap-RR-38-2013,
         author = {Motlicek, Petr and Garner, Philip N. and Kim, Namhoon and Cho, Jeongmi},
       keywords = {Accented speech, Acoustic model adaptation, Automatic Speech Recognition, Under-resourced data},
       projects = {Idiap, SAMSUNG},
          month = {11},
          title = {ACCENT ADAPTATION USING SUBSPACE GAUSSIAN MIXTURE MODELS},
           type = {Idiap-RR},
         number = {Idiap-RR-38-2013},
           year = {2013},
    institution = {Idiap},
        address = {Rue Marconi 19, Martigny, Switzerland},
       abstract = {This paper investigates employment of Subspace Gaussian Mixture Models (SGMMs) for acoustic model adaptation towards different accents for English speech recognition. The SGMMs comprise globally-shared and state-specific parameters which can efficiently be employed for various kinds of acoustic parameter tying. Research results indicate that well-defined sharing of acoustic model parameters in SGMMs can significantly outperform adapted systems based on conventional HMM/GMMs. Furthermore, SGMMs rapidly achieve target acoustic models with small amounts of data. Experiments performed with US and UK English versions of the
Wall Street Journal (WSJ) corpora indicate that SGMMs lead to approximately 20\% and 8\% relative improvements with respect to speaker-independent and speaker-adapted acoustic models respectively over conventional HMM/GMMs. Finally, we demonstrate that SGMMs adapted only with 1.5 hours can reach performance of HMM/GMMs trained with 18 hours.},
            pdf = {https://publications.idiap.ch/attachments/reports/2013/Motlicek_Idiap-RR-38-2013.pdf}
}