自动化与仪器仪表2024,Issue(4) :197-200.DOI:10.14016/j.cnki.1001-9227.2024.04.197

基于Bi-LSTM和Softmax的辅助发声训练系统设计研究

Design and Research of an Assisted Vocal Training System Based on Bi-LSTM and Softmax

李可
自动化与仪器仪表2024,Issue(4) :197-200.DOI:10.14016/j.cnki.1001-9227.2024.04.197

基于Bi-LSTM和Softmax的辅助发声训练系统设计研究

Design and Research of an Assisted Vocal Training System Based on Bi-LSTM and Softmax

李可1
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作者信息

  • 1. 陕西工业职业技术学院,陕西咸阳 712000
  • 折叠

摘要

为进一步提升英语口语发音训练的辅助效果,提出一种基于双向多轮信息共享网络IN-SN的口语理解模型,辅助学习者进行英语口语的发声训练.其中,以Bi-LSTM为基础的语义信息提取方法,结合双向多轮信息共享网络和注意力机制,以实现更佳的口语理解效果.实验结果表明,与其他语义提取模型相比,设计的基于IN-SN的口语理解模型具有更好的语义提取能力,在数据集ATIS和数据集SNPIS上的准确率和F1评分上分别达到了 96.06%、95.98%、97.47%、94.03%;设计的英语口语辅助训练系统能够进行准确意图识别和语义槽填充.综上,设计的英语口语辅助训练系统性能良好,能够应用于实际的英语口语发声学习场景中,可行性较高.

Abstract

To further improve the auxiliary effect of English oral pronunciation training,a bidirectional multi round information sharing network IN-SN based oral comprehension model is proposed to assist learners in English oral pronunciation training.Among them,the semantic information extraction method based on Bi LSTM combines bidirectional multi round information sharing network and attention mechanism to achieve better oral comprehension effects.The experimental results show that compared with other seman-tic extraction models,the designed IN-SN based oral comprehension model has better semantic extraction ability,achieving accuracy and F1 scores of 96.06%,95.98%,97.47%,and 94.03%on the dataset ATIS and dataset SNPIS,respectively;The designed Eng-lish speaking auxiliary training system can perform accurate intention recognition and semantic slot filling.In summary,the designed English speaking auxiliary training system has good performance and can be applied to practical English speaking vocalization learning scenarios,with high feasibility.

关键词

英语口语/辅助训练/语义提取/信息共享网络

Key words

spoken English/auxiliary training/semantic extraction/information sharing network

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基金项目

陕西省中华职业教育社研究课题(ZJS202324)

教育教指委项目(JYJZWZW-2023B-02)

中国教育国际交流协会项目(GJZJ2021-12)

陕西工业职业技术学院项目(2022YKYB-060)

出版年

2024
自动化与仪器仪表
重庆工业自动化仪表研究所,重庆市自动化与仪器仪表学会

自动化与仪器仪表

CSTPCD
影响因子:0.327
ISSN:1001-9227
参考文献量12
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