论文标题
使用变压器的基于脑电图的连续语音识别
EEG based Continuous Speech Recognition using Transformers
论文作者
论文摘要
在本文中，我们使用最近引入的基于端到端变压器的自动语音识别（ASR）模型的脑电图（EEG）特征研究了连续的语音识别。我们的结果表明，基于变压器的模型与基于复发性神经网络（RNN）基于序列序列的序列EEG模型相比，训练速度更快，并且在推理期间的较小测试集词汇的推理期间的性能更好，但是随着我们的词汇量的增加，基于RNN的模型的性能比有限的英语vocabulary的模型更好。
In this paper we investigate continuous speech recognition using electroencephalography (EEG) features using recently introduced end-to-end transformer based automatic speech recognition (ASR) model. Our results demonstrate that transformer based model demonstrate faster training compared to recurrent neural network (RNN) based sequence-to-sequence EEG models and better performance during inference time for smaller test set vocabulary but as we increase the vocabulary size, the performance of the RNN based models were better than transformer based model on a limited English vocabulary.
