Ulrich Bodenhausen

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In this work we describe a new method that adjusts time-delays and the widths of time-windows in artificial neural networks automatically. The input of the units are weighted by a gaussian input-window over time which allows the learning rules for the delays and widths to be derived in the same way as it is used for the weights. Our results on a phoneme(More)
In this paper we show how the Multi-State Time Delay Neural Network (MS-TDNN), which is already used successfully in continuous speech recognition tasks, can be applied both to on-line single character and cursive (continuous) handwriting recognition. The MS-TDNN integrates the high accuracy single character recognition capabilities of a TDNN with a(More)
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