## Frontmatter | | | | --- | --- | | Authors | [[Julia Berezutskaya]], [[Zachary Freudenburg]], [[Nick Ramsey]], [[Umut Güçlü]], [[Marcel van Gerven]] | | Date | 2017/06 | | Source | [[Benelux Conference on Machine Learning]] | | URL | https://pure.tue.nl/ws/files/72619856/benelearn_2017.pdf#page=150 | | Citation | Berezutskaya, J., Freudenburg, Z., Ramsey, N., Güçlü, U., & van Gerven, M. (2017). [[Modeling brain responses to perceived speech with LSTM networks]]. In _Benelux Conference on Machine Learning_. [[URL](https://pure.tue.nl/ws/files/72619856/benelearn_2017.pdf#page=150)]. #Conference | ## Abstract We used recurrent neural networks with long short-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. ## PDF ![[Modeling brain responses to perceived speech with LSTM networks.pdf]]