Speech Recognition with no speech or with noisy speech
This addresses the challenge of robust speech recognition for users in noisy environments, though it appears incremental as it builds on existing EEG and ASR methods.
The paper tackles the problem of automatic speech recognition (ASR) performance degradation in noisy conditions by using electroencephalography (EEG) to enhance ASR systems, achieving high accuracy in recognizing words from EEG without speech on a limited vocabulary.
The performance of automatic speech recognition systems(ASR) degrades in the presence of noisy speech. This paper demonstrates that using electroencephalography (EEG) can help automatic speech recognition systems overcome performance loss in the presence of noise. The paper also shows that distillation training of automatic speech recognition systems using EEG features will increase their performance. Finally, we demonstrate the ability to recognize words from EEG with no speech signal on a limited English vocabulary with high accuracy.