%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Garner_ASRU_2009,
                      author = {Garner, Philip N.},
                    projects = {IM2},
                       month = {12},
                       title = {SNR Features for Automatic Speech Recognition},
                   booktitle = {Proceedings of the IEEE workshop on Automatic Speech Recognition and Understanding},
                        year = {2009},
                    location = {Merano, Italy},
                    crossref = {Garner_Idiap-RR-25-2009},
                    abstract = {When combined with cepstral normalisation techniques, the features
  normally used in Automatic Speech Recognition are based on Signal to
  Noise Ratio (SNR).  We show that calculating SNR from the outset,
  rather than relying on cepstral normalisation to produce it, gives
  features with a number of practical and mathematical advantages over
  power-spectral based ones.  In a detailed analysis, we derive
  Maximum Likelihood and Maximum a-Posteriori estimates for SNR based
  features, and show that they can outperform more conventional ones,
  especially when subsequently combined with cepstral variance
  normalisation.  We further show anecdotal evidence that SNR based
  features lend themselves well to noise estimates based on low-energy
  envelope tracking.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2009/Garner_ASRU_2009.pdf}
}



crossreferenced publications: 
@TECHREPORT{Garner_Idiap-RR-25-2009,
                      author = {Garner, Philip N.},
                    projects = {IM2},
                       month = {9},
                       title = {SNR Features for Automatic Speech Recognition},
                        type = {Idiap-RR},
                      number = {Idiap-RR-25-2009},
                        year = {2009},
                 institution = {Idiap},
                    abstract = {When combined with cepstral normalisation techniques, the features
  normally used in Automatic Speech Recognition are based on Signal to
  Noise Ratio (SNR).  We show that calculating SNR from the outset,
  rather than relying on cepstral normalisation to produce it, gives
  features with a number of practical and mathematical advantages over
  power-spectral based ones.  In a detailed analysis, we derive
  Maximum Likelihood and Maximum a-Posteriori estimates for SNR based
  features, and show that they can outperform more conventional ones,
  especially when subsequently combined with cepstral variance
  normalisation.  We further show anecdotal evidence that SNR based
  features lend themselves well to noise estimates based on low-energy
  envelope tracking.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2009/Garner_Idiap-RR-25-2009.pdf}
}