To resolve the problem of voice command recognition errors existing in the speech interaction system for YSU-Ⅱ lower limb rehabilitation robot,a Bidirectional Gated Recurrent Unit(Bi-GRU)based Seq2Seq model was proposed to detect and correct the errors in instruction text.In addition,a Contextual and Keywords-based Attention(CK Attention)mechanism was proposed to enhance the performance of instruction text proofreading model.To objectively evaluate the performance of the model,a corpus for rehabilitation robot training tasks was es-tablished,and five 5-fold cross-validation method was employed to conduct a series of experiments on the corpus.The experimental results demonstrated that the Bi-GRU based Seq2Seq model was applicable for the instruction text proofreading task,and the CK Attention mechanism contributed to improve the performance of the text proofreading model.The detection F1 and the correction F1 of the proposed model had reached 97.72％and 93.89％respectively.The processing time of the instruction text proofreading model for common instructions was 0.156 s～0.391 s.
关键词
文本校对/语音交互/Seq2Seq/双向门控循环单元/注意力机制
Key words
text proofreading/speech interaction/Seq2Seq/bidirectional gated recurrent unit/attention mechanism
维普
万方数据