Automatic Speech Activity Recognition from MEG Signals Using Seq2Seq Learning

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dc.contributor.ORCID0000-0001-7265-217X (Wang, J)
dc.contributor.authorDash, Debadatta
dc.contributor.authorFerrari, P.
dc.contributor.authorMalik, S.
dc.contributor.authorWang, Jun
dc.contributor.utdAuthorDash, Debadatta
dc.contributor.utdAuthorWang, Jun
dc.date.accessioned2020-03-10T19:57:38Z
dc.date.available2020-03-10T19:57:38Z
dc.date.issued2019-03
dc.descriptionDue to copyright restrictions and/or publisher's policy full text access from Treasures at UT Dallas is limited to current UTD affiliates (use the provided Link to Article).
dc.description.abstractAccurate interpretation of speech activity from brain signals is critical for understanding the relationship between neural patterns and speech production. Current research on speech activity recognition from the brain activity heavily relies on the region of interest (ROI) based functional connectivity analysis or source separation strategies to map the activity as a spatial localization of a brain function. Albeit effective, these methods require prior knowledge of the brain and expensive computational effort. In this study, we investigated automatic speech activity recognition from brain signals using machine learning. Neural signals of four subjects during four stages of a speech task (i.e., rest, perception, preparation, and production) were recorded using magnetoencephalography (MEG), which has an excellent temporal and spatial resolution. First, a deep neural network (DNN) was used to classify the four whole tasks from the MEG signals. Further, we trained a sequence to sequence (Seq2Seq) long short-term memory-recurrent neural network (LSTM-RNN) for continuous (sample by sample) prediction of the speech stages/tasks by leveraging its sequential pattern learning paradigm. Experimental results indicate the effectiveness of both DNN and LSTM-RNN for automatic speech activity recognition from MEG signals. © 2019 IEEE.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.description.departmentCallier Center for Communication Disorders
dc.description.sponsorshipNational Institutes of Health (NIH) under award number R03 DC013990
dc.identifier.bibliographicCitationDash, D., P. Ferrari, S. Malik, and J. Wang. 2019. "Automatic Speech Activity Recognition from MEG Signals Using Seq2Seq Learning." International IEEE/EMBS Conference on Neural Engineering, 9th: 340-343, doi: 10.1109/NER.2019.8717186
dc.identifier.issn9781538679210
dc.identifier.urihttp://dx.doi.org/10.1109/NER.2019.8717186
dc.identifier.urihttps://hdl.handle.net/10735.1/7384
dc.identifier.volume2019
dc.language.isoen
dc.publisherIEEE Computer Society
dc.relation.isPartOfInternational IEEE/EMBS Conference on Neural Engineering, 9th
dc.rights©2019 IEEE
dc.subjectBrain
dc.subjectBrain mapping
dc.subjectNeural networks (Neurobiology)
dc.subjectImage segmentation
dc.subjectLong-term memory
dc.subjectMagnetoencephalography
dc.subjectSpeech
dc.subjectAutomatic speech recognition
dc.subjectSpeech perception
dc.subjectShort-term memory
dc.titleAutomatic Speech Activity Recognition from MEG Signals Using Seq2Seq Learning
dc.type.genrearticle

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