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Millions of people with severe speech disorders around the world may regaintheir communication capabilities through techniques of silent speechrecognition (SSR). Using electroencephalography (EEG) as a biomarker for speechdecoding has been popular for SSR. However, the lack of SSR text corpus hasimpeded the development of this technique. Here, we construct a noveltask-oriented text corpus, which is utilized in the field of SSR. In theprocess of construction, we propose a task-oriented hybrid construction methodbased on natural language generation algorithm. The algorithm focuses on thestrategy of data-to-text generation, and has two advantages includinglinguistic quality and high diversity. These two advantages use template-basedmethod and deep neural networks respectively. In an SSR experiment with thegenerated text corpus, analysis results show that the performance of our hybridconstruction method outperforms the pure method such as template-based naturallanguage generation or neural natural language generation models.

A Novel Task-Oriented Text Corpus in Silent Speech Recognition and its Natural Language Generation Construction Method