%0 Conference Paper %1 graves+beringer+schmidhuber:2003 %A Graves, A. %A Beringer, N. %A Schmidhuber, J. %B The 23rd IASTED International Conference on modelling, identification, and control %C Grindelwald %D 2003 %K juergen %T A Comparison Between Spiking and Differentiable Recurrent Neural Networks on Spoken Digit Recognition %X In this paper we demonstrate that Long Short-Term Memory (LSTM) is a differentiable recurrent neural net (RNN) capable of robustly categorizing timewarped speech data. We measure its performance on a spoken digit identification task, where the data was spike-encoded in such a way that classifying the utterances became a difficult challenge in non-linear timewarping. We find that LSTM gives greatly superior results to an SNN found in the literature, and conclude that the architecture has a place in domains that require the learning of large timewarped datasets, such as automatic speech recognition.

@inproceedings{graves+beringer+schmidhuber:2003, abstract = {In this paper we demonstrate that Long Short-Term Memory (LSTM) is a differentiable recurrent neural net (RNN) capable of robustly categorizing timewarped speech data. We measure its performance on a spoken digit identification task, where the data was spike-encoded in such a way that classifying the utterances became a difficult challenge in non-linear timewarping. We find that LSTM gives greatly superior results to an SNN found in the literature, and conclude that the architecture has a place in domains that require the learning of large timewarped datasets, such as automatic speech recognition.}, added-at = {2008-03-11T14:52:34.000+0100}, address = {Grindelwald}, author = {Graves, A. and Beringer, N. and Schmidhuber, J.}, biburl = {https://www.bibsonomy.org/bibtex/20d722f3925b1c8f267da7f14fe837e0c/idsia}, booktitle = {The 23rd IASTED International Conference on modelling, identification, and control}, citeulike-article-id = {2381716}, interhash = {3dcaec8492752c6f4529704ecbe6fa5b}, intrahash = {0d722f3925b1c8f267da7f14fe837e0c}, keywords = {juergen}, priority = {2}, timestamp = {2008-03-11T14:53:44.000+0100}, title = {A Comparison Between Spiking and Differentiable Recurrent Neural Networks on Spoken Digit Recognition}, year = 2003 }