DocumentCode :
3527388
Title :
Bounded conditional mean imputation with Gaussian mixture models: A reconstruction approach to partly occluded features
Author :
Faubel, Friedrich ; McDonough, John ; Klakow, Dietrich
Author_Institution :
Spoken Language Syst., Saarland Univ., Saarbrucken
fYear :
2009
fDate :
19-24 April 2009
Firstpage :
3869
Lastpage :
3872
Abstract :
In this work we show how conditional mean imputation can be bounded through the use of box-truncated Gaussian distributions. That is of interest when signals or features are partly occluded by a superimposed interference, as then the noisy observation poses an upper bound. Unfortunately, the occurring integrals are not analytic. Hence an approximate solution has to be used. In the experimental section we apply the bounded approach to the reconstruction of partly occluded speech spectra and demonstrate its superiority over the unbounded case with respect to automatic speech recognition performance.
Keywords :
Gaussian distribution; signal reconstruction; speech recognition; Gaussian mixture models; automatic speech recognition performance; box-truncated Gaussian distributions; signal reconstruction; speech spectra; Automatic speech recognition; Gaussian distribution; Image reconstruction; Interference; Mean square error methods; Natural languages; Signal reconstruction; Speech enhancement; Speech recognition; Upper bound; Gaussian distributions; Mean square error methods; Signal reconstruction; speech enhancement; speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
ISSN :
1520-6149
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2009.4960472
Filename :
4960472
Link To Document :
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