CONF
Garner_ASRU_2009/IDIAP
SNR Features for Automatic Speech Recognition
Garner, Philip N.
EXTERNAL
https://publications.idiap.ch/attachments/papers/2009/Garner_ASRU_2009.pdf
PUBLIC
https://publications.idiap.ch/index.php/publications/showcite/Garner_Idiap-RR-25-2009
Related documents
Proceedings of the IEEE workshop on Automatic Speech Recognition and Understanding
Merano, Italy
2009
December 2009
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.
REPORT
Garner_Idiap-RR-25-2009/IDIAP
SNR Features for Automatic Speech Recognition
Garner, Philip N.
EXTERNAL
https://publications.idiap.ch/attachments/reports/2009/Garner_Idiap-RR-25-2009.pdf
PUBLIC
Idiap-RR-25-2009
2009
Idiap
September 2009
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.