Modeling brain responses to perceived speech with LSTM networks
[S.l.] : [S.n.]
InDuivesteijn, W.; Pechenizkiy, M.; Fletcher, G.H.L. (ed.), Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning, Technische Universiteit Eindhoven, 9-10 June 2017, pp. 149-153
Benelearn 2017: Twenty-Sixth Benelux Conference on Machine Learning (Eindhoven, Netherlands, 9-10 June 2017)
Article in monograph or in proceedings
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Putten, P. van der
SW OZ DCC AI
Duivesteijn, W.; Pechenizkiy, M.; Fletcher, G.H.L. (ed.), Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning, Technische Universiteit Eindhoven, 9-10 June 2017
SubjectCognitive artificial intelligence; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication; Language in Interaction
We used recurrent neural networks with longshort term memory units (LSTM) to model the brain responses to speech based on the speech audio features. We compared the performance of the LSTM models to the performance of the linear ridge regression model and found the LSTM models to be more robust for predicting brain responses across different feature sets.
NWO (Grant code:info:eu-repo/grantAgreement/NWO/Gravitation/024.001.006)
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