论文标题
用于改善基于脑电图的语音识别系统的差异自动编码器受限
Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems
论文作者
论文摘要
在本文中，我们介绍了具有新的约束损耗函数的基于反复的神经网络（RNN）变异自动编码器（VAE）模型，该模型可以生成来自原始EEG特征的更有意义的脑电图（EEG）功能，以提高基于EEG的语音识别系统的性能。我们证明，使用RAW EEG功能产生的EEG功能训练和测试的连续和孤立的语音识别系统都使用我们的VAE模型产生的EEG功能可改善性能，并且我们为有限的英语词汇表明了我们的结果，该词汇包括30种独特的句子，包括连续语音识别的30个独特的句子，以及用于由2个独特语音识别的英语词汇组成的，该句子由2个独特的句子组成。我们将我们的方法与[1]中作者在[1]中描述的另一种最近介绍的方法进行了比较，以提高基于EEG的连续语音识别系统的性能，并且我们证明我们的方法在使用相同的数据集进行训练和测试时，随着词汇大小的增加而优于其方法。即使我们仅在本文中证明了自动语音识别（ASR）实验的结果，但具有约束损失函数的VAE模型可以扩展到其他各种基于EEG的大脑计算机界面（BCI）应用程序。
In this paper we introduce a recurrent neural network (RNN) based variational autoencoder (VAE) model with a new constrained loss function that can generate more meaningful electroencephalography (EEG) features from raw EEG features to improve the performance of EEG based speech recognition systems. We demonstrate that both continuous and isolated speech recognition systems trained and tested using EEG features generated from raw EEG features using our VAE model results in improved performance and we demonstrate our results for a limited English vocabulary consisting of 30 unique sentences for continuous speech recognition and for an English vocabulary consisting of 2 unique sentences for isolated speech recognition. We compare our method with another recently introduced method described by authors in [1] to improve the performance of EEG based continuous speech recognition systems and we demonstrate that our method outperforms their method as vocabulary size increases when trained and tested using the same data set. Even though we demonstrate results only for automatic speech recognition (ASR) experiments in this paper, the proposed VAE model with constrained loss function can be extended to a variety of other EEG based brain computer interface (BCI) applications.
