Continuous Speech Recognition using EEG and Video
This work addresses speech recognition challenges for systems relying on visual cues, but it appears incremental as it builds on existing methods without specifying major breakthroughs.
The paper tackled the problem of continuous visual speech recognition by investigating whether EEG features could improve system performance, and found that EEG features enhanced the recognition results.
In this paper we investigate whether electroencephalography (EEG) features can be used to improve the performance of continuous visual speech recognition systems. We implemented a connectionist temporal classification (CTC) based end-to-end automatic speech recognition (ASR) model for performing recognition. Our results demonstrate that EEG features are helpful in enhancing the performance of continuous visual speech recognition systems.