  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.
