Speech Recognition With No Speech Or With Noisy Speech Beyond English
This addresses speech recognition challenges in noisy or silent environments, but is incremental as it builds on existing EEG feature methods.
The paper tackles continuous noisy speech recognition without audio input by using EEG features, achieving results on limited Chinese vocabulary and joint English-Chinese vocabulary with a novel deep learning architecture.
In this paper we demonstrate continuous noisy speech recognition using connectionist temporal classification (CTC) model on limited Chinese vocabulary using electroencephalography (EEG) features with no speech signal as input and we further demonstrate single CTC model based continuous noisy speech recognition on limited joint English and Chinese vocabulary using EEG features with no speech signal as input. We demonstrate our results using various EEG feature sets recently introduced in [1] as well as we propose a new deep learning architecture in this paper which can perform continuous speech recognition using raw EEG signals on limited joint English and Chinese vocabulary.