Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recording
CoRR(2024)
Abstract
Relating speech to EEG holds considerable importance but challenging. In this
study, deep convolutional network was employed to extract spatiotemporal
features from EEG data. Self-supervised speech representation and contextual
text embedding were used as speech features. Contrastive learning was used to
related EEG features to speech features. The experimental results demonstrate
the benefits of using self-supervised speech representation and contextual text
embedding. Through feature fusion and model ensemble, an accuracy of 60.29
achieved, and the performance was ranked as No.2 in Task1 of the Auditory EEG
Challenge (ICASSP 2024).
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined