%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 01:00:29 PM

@INPROCEEDINGS{Pinto_ICASSP_2009,
         author = {Pinto, Joel Praveen and Sivaram, G. S. V. S. and Hermansky, Hynek and Magimai.-Doss, Mathew},
       projects = {Idiap, SNSF-KEYSPOT, SNSF-MULTI, IM2},
          title = {Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator},
      booktitle = {Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
           year = {2009},
       crossref = {Pinto_Idiap-RR-69-2008},
       abstract = {We present a framework to apply Volterra series to analyze multilayered perceptrons trained to estimate the posterior probabilities of phonemes in automatic speech recognition. The identified Volterra kernels reveal the spectro-temporal patterns that are learned by the trained system for each phoneme. To demonstrate the applicability of Volterra series, we analyze a multilayered perceptron trained using Mel filter bank energy features and analyze its first order Volterra kernels.},
            pdf = {https://publications.idiap.ch/attachments/papers/2009/Pinto_ICASSP_2009.pdf}
}



crossreferenced publications: 
@TECHREPORT{Pinto_Idiap-RR-69-2008,
         author = {Pinto, Joel Praveen and Sivaram, G. S. V. S. and Hermansky, Hynek and Magimai.-Doss, Mathew},
       projects = {SNSF-KEYSPOT, IM2, SNSF-MULTI},
          month = {10},
          title = {Volterra Series for Analyzing MLP based Phoneme Posterior Probability Estimator},
           type = {Idiap-RR},
         number = {Idiap-RR-69-2008},
           year = {2008},
    institution = {Idiap},
       abstract = {We present a framework to apply Volterra series to analyze multilayered perceptrons trained to estimate the posterior probabilities of phonemes in automatic speech recognition. The identified Volterra kernels reveal the spectro-temporal patterns that are learned by the trained system for each phoneme. To demonstrate the applicability of Volterra series, we analyze a multilayered perceptron trained using Mel filter bank energy features and analyze its first order Volterra kernels.},
            pdf = {https://publications.idiap.ch/attachments/reports/2008/Pinto_Idiap-RR-69-2008.pdf}
}