%Aigaion2 BibTeX export from Idiap Publications
%Saturday 21 December 2024 04:38:27 PM

@TECHREPORT{Motlicek_Idiap-RR-20-2012,
         author = {Motlicek, Petr and Garner, Philip N. and Imseng, David and Valente, Fabio},
       keywords = {acoustic modeling, Automatic Speech Recognition, Subs-ace Gaussian Mixture Models},
       projects = {Idiap},
          month = {7},
          title = {Application of Subspace Gaussian Mixture Models in Contrastive Acoustic Scenarios},
           type = {Idiap-RR},
         number = {Idiap-RR-20-2012},
           year = {2012},
    institution = {Idiap},
        address = {Rue Marconi 19, Martigny, Switzerland},
       abstract = {This paper describes experimental results of applying Subspace
Gaussian Mixture Models (SGMMs) in two completely diverse
acoustic scenarios: (a) for Large Vocabulary Continuous
Speech Recognition (LVCSR) task over (well-resourced)
English meeting data and, (b) for acoustic modeling of underresourced
Afrikaans telephone data. In both cases, the performance
of SGMM models is compared with a conventional
context-dependent HMM/GMM approach exploiting the same
kind of information available during the training. LVCSR
systems are evaluated on standard NIST Rich Transcription
dataset. For under-resourced Afrikaans, SGMM and
HMM/GMM acoustic systems are additionally compared to
KL-HMM and multilingual Tandem techniques boosted using
supplemental out-of-domain data. Experimental results clearly
show that the SGMMapproach (having considerably less model
parameters) outperforms conventional HMM/GMM system in
both scenarios and for all examined training conditions. In case
of under-resourced scenario, the SGMM trained only using indomain
data is superior to other tested approaches boosted by
data from other domain.},
            pdf = {https://publications.idiap.ch/attachments/reports/2012/Motlicek_Idiap-RR-20-2012.pdf}
}