%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 12:50:39 PM

@INPROCEEDINGS{astrid-00-21b,
         author = {Hagen, Astrid and Morris, Andrew},
       keywords = {full combination, HMM/ANN-Hybrid, multi-band, multi-stream},
       projects = {Idiap},
          title = {Comparison of HMM experts with MLP experts in the Full Combination Multi-Band Approach to Robust ASR},
      booktitle = {ICSLP},
           year = {2000},
           note = {IDIAP-RR 00-21},
       crossref = {astrid-00-21a},
       abstract = {In this paper we apply the Full Combination (FC) multi-band approach, which has originally been introduced in the framework of posterior-based HMM/ANN (Hidden Markov Model/Artificial Neural Network) hybrid systems, to systems in which the ANN (or Multilayer Perceptron (MLP)) is itself replaced by a Multi Gaussian HMM (MGM). Both systems represent the most widely used statistical models for robust ASR (automatic speech recognition). It is shown how the FC formula for the likelihood--based MGMs can easily be derived from the posterior-based approach by simply applying Bayes' Rule. The experiments show that the Full Combination multi-band system with MGM experts performs better, in all noise conditions tested, than the simple sum and product rules which are normally used. As compared to the baseline full-band system, the FC system shows increased robustness mainly on band-limited noise. The goal of this article is not a performance comparison between Multilayer Perceptrons and Multi Gaussian Models but between the theory of the two approaches, posterior-based vs. likelihood-based FC approach, so results are only given for the MGMs.},
            pdf = {https://publications.idiap.ch/attachments/reports/2000/rr00-21.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2000/rr00-21.ps.gz},
ipdmembership={speech},
language={English},
}



crossreferenced publications: 
@TECHREPORT{astrid-00-21a,
         author = {Hagen, Astrid and Morris, Andrew},
       keywords = {full combination, HMM/ANN-Hybrid, multi-band, multi-stream},
       projects = {Idiap},
          month = {7},
          title = {Comparison of HMM experts with MLP experts in the Full Combination Multi-Band Approach to Robust ASR},
           type = {Idiap-RR},
         number = {Idiap-RR-21-2000},
           year = {2000},
    institution = {IDIAP},
        address = {Martigny, Switzerland},
           note = {Published: ICSLP 2000, Beijing, September 2000},
       abstract = {In this paper we apply the Full Combination (FC) multi-band approach, which has originally been introduced in the framework of posterior-based HMM/ANN (Hidden Markov Model/Artificial Neural Network) hybrid systems, to systems in which the ANN (or Multilayer Perceptron (MLP)) is itself replaced by a Multi Gaussian HMM (MGM). Both systems represent the most widely used statistical models for robust ASR (automatic speech recognition). It is shown how the FC formula for the likelihood-based MGMs can easily be derived from the posterior-based approach by simply applying Bayes' Rule. The experiments show that the Full Combination multi-band system with MGM experts performs better, in all noise conditions tested, than the simple sum and product rules which are normally used. As compared to the baseline full-band system, the FC system shows increased robustness mainly on band-limited noise. The goal of this article is not a performance comparison between Multilayer Perceptrons and Multi Gaussian Models but between the theory of the two approaches, posterior-based vs. likelihood-based FC approach, so results are only given for the MGMs.},
            pdf = {https://publications.idiap.ch/attachments/reports/2000/rr00-21.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2000/rr00-21.ps.gz},
ipdinar={2000},
ipdmembership={speech},
language={English},
}