Abstract
Millions of people with severe speech disorders around the world may regain their communication capabilities through techniques of silent speech recognition (SSR). Using electroencephalography (EEG) as a biomarker for speech decoding has been popular for SSR. However, the lack of SSR text corpus has impeded the development of this technique. Here, we construct a novel task-oriented text corpus, which is utilized in the field of SSR. In the process of construction, we propose a task-oriented hybrid construction method based on natural language generation algorithm. The algorithm focuses on the strategy of data-to-text generation, and has two advantages including linguistic quality and high diversity. These two advantages use template-based method and deep neural networks respectively. In an SSR experiment with the generated text corpus, analysis results show that the performance of our hybrid construction method outperforms the pure method such as template-based natural language generation or neural natural language generation models.
Abstract (translated)
全世界数百万患有严重语言障碍的人可能通过无声语音识别（SSR）技术重新获得他们的沟通能力。脑电图（EEG）作为语音解码的生物标志物已成为SSR研究的热点。然而，SSR文本语料库的缺乏阻碍了该技术的发展。在此，我们构建了一个新的面向任务的文本语料库，并将其应用于SSR领域。在构造过程中，提出了一种基于自然语言生成算法的面向任务的混合构造方法。该算法注重数据到文本的生成策略，具有语言质量高、多样性高等优点。这两个优点分别采用了基于模板的方法和深度神经网络。在对生成的文本语料库进行的SSR实验中，分析结果表明，混合构造方法的性能优于基于模板的自然语言生成或神经自然语言生成模型等纯方法。
URL
https://arxiv.org/abs/1905.01974
