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Abstract:
In the `missing data' approach to improving the robustness of
automatic speech recognition to added noise, an initial process
identifies spectral temporal regions which are dominated by the
speech source. The remaining regions are considered to be
`missing'. In this paper we develop a connectionist approach to
the problem of adapting speech recognition to the missing data
case, using Recurrent Neural Networks. In contrast to methods
based on Hidden Markov Models, RNNs allow us to make use of
long-term time constraints and to make the problems of
classification with incomplete data and imputing missing values
interact. We report encouraging results on an isolated digit
recognition task.
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