CONF
icslp2000/IDIAP
A neural network for classification with incomplete data: application to robust ASR
Morris, Andrew
Josifovski, Ljubomir
Bourlard, HervĂ©
Cooke, Martin
Green, Phil
missing features
neural networks
robust recognition
EXTERNAL
http://publications.idiap.ch/attachments/reports/2000/morris-2000-icslp.pdf
PUBLIC
http://publications.idiap.ch/index.php/publications/showcite/morris-rr-00-23
Related documents
Proc. ICSLP
2000
Beijing, China
If the data vector for input to an automatic classifier is incomplete, the optimal estimate for each class probability must be calculated as the expected value of the classifier output. We identify a form of RBF classifier whose expected outputs can easily be evaluated in terms of the original function parameters. We then describe two ways in which this classifier can be applied to robust automatic speech recognition, depending on whether or not the position of missing data is known
REPORT
morris-RR-00-23/IDIAP
A neural network for classification with incomplete data
Morris, Andrew
missing features
neural networks
robust recognition
EXTERNAL
http://publications.idiap.ch/attachments/reports/2000/rr00-23.pdf
PUBLIC
Idiap-RR-23-2000
2000
IDIAP
If the data vector for input to an automatic classifier is incomplete, the optimal estimate for each class probability must be calculated as the expected value of the classifier output. We identify a form of Radial Basis Function (RBF) classifier whose expected outputs can easily be evaluated in terms of the original function parameters. Two ways are described in which this classifier can be applied to robust automatic speech recognition, depending on whether or not the position of missing data is known.