A Novel Task-Oriented Text Corpus in Silent Speech Recognition and its Natural Language Generation Construction Method
This addresses a bottleneck for researchers developing SSR systems to aid people with severe speech disorders, though it is incremental as it builds on existing generation techniques.
The authors tackled the lack of text corpora for silent speech recognition (SSR) by constructing a novel task-oriented corpus using a hybrid natural language generation method, which outperformed pure template-based or neural models in SSR experiments.
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.