首页|面向YSU-Ⅱ下肢康复机器人语音交互系统的指令文本校对模型

面向YSU-Ⅱ下肢康复机器人语音交互系统的指令文本校对模型

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  • 维普
针对YSU-Ⅱ下肢康复机器人语音交互系统存在指令误识的问题,构建了基于双向门控循环单元的Seq2Seq模型来检测并纠正指令文本中的错误字符,提出一种基于指令上下文和关键字的注意力机制(CK Atten-tion),用于捕获指令文本的上下文语义和关键字信息,以提升模型的文本校对能力.面向康复机器人的训练任务自行建立了语料库,并采用5次5折交叉验证法在该语料库上开展了相关实验,以客观评估模型性能.实验结果表明,所建模型适用于指令文本校对任务,CK Attention机制能够有效提升模型的文本校对性能,其检错F1值和纠错F1值分别达到97.72％和93.89％,对常用指令文本的校对时长在0.156 s～0.391 s之间.
Instruction text proofreading model oriented to speech interaction system for YSU-Ⅱ lower limb rehabilitation robot
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.

text proofreadingspeech interactionSeq2Seqbidirectional gated recurrent unitattention mechanism

仲美玉、吴培良、窦燕、张晓丹、孔令富

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燕山大学信息科学与工程学院,河北 秦皇岛 066004

河北省计算机虚拟技术与系统集成重点实验室,河北 秦皇岛 066004

河北省软件工程重点实验室,河北 秦皇岛 066004

文本校对 语音交互 Seq2Seq 双向门控循环单元 注意力机制

国家重点研发计划资助项目国家自然科学基金区域联合基金资助项目北京市自然科学基金资助项目河北省自然科学基金资助项目河北省创新能力提升计划资助项目河北省软件工程重点实验室资助项目

2018YFB1308300U20A201674202026F20210307922567626H22567637H

2024

10.13196/j.cims.2022.0143

计算机集成制造系统
中国兵器工业集团第210研究所

计算机集成制造系统

CSTPCD北大核心
影响因子：1.092
ISSN：1006-5911
年,卷(期)：2024.30(10)