'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
Missing data theory (MDT) has been applied to handle the
problem of noise-robust speech recognition. Conventional
MDT-systems require acoustic models that are expressed in
the log-spectral rather than in the cepstral domain, which leads
to a loss in accuracy. Therefore, we have already introduced
a MDT-technique that can be applied in any feature domain
that is a linear transform of log-spectra. This MDT-system
requires hard decisions about the reliability of each spectral
component. When computed from noisy data, misclassification
errors in the mask are hardly unavoidable and the recognition
rate will significantly degrade. The risk of misclassifications
can be reduced by estimating a probability that
the component is reliable, e.g. a fuzzy mask. In this paper,
we extend our MDT-system to be applied in the probabilistic
decision framework. Experiments on the Aurora2 database
demonstrate a further increase in recognition accuracy, especially
at low SNRs.Van Segbroeck M., Van hamme H., ''Robust speech recognition using missing data techniques in the PROSPECT domain and fuzzy masks'', Proceedings IEEE international conference on acoustics, speech, and signal processing - ICASSP’2008, pp. 4393-4396, March 30 - April 4, 2008, Las Vegas, Nevada, USA.status: publishe