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人工智能在同声传译自主学习中的作用
The Role of Artificial Intelligence in Autonomous Simultaneous Interpretation Learning

DOI: 10.12677/ae.2025.151087, PP. 627-633

Keywords: 语音识别,机器翻译,自主学习,同声传译
Voice Recognition
, Machine Translation, Autonomous Learning, Simultaneous Interpretation

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Abstract:

随着人工智能技术的飞速发展,语音识别和机器翻译技术日渐成熟并且不可避免地逐渐进入英语课堂。同声传译课程中涉及的听力理解和即时翻译两大基本学习过程也是受到语音识别和机器翻译技术冲击最直接的两个方面。因此,教师在教学过程互动和教学结果评估等多方面需要做出适应性的改变。本研究基于齐莫曼的自主学习模型,设计了一种将语音识别和机器翻译等人工智能技术融入同声传译自主学习模式的方法,探讨语音识别和机器翻译等人工智能技术对同声传译自主学习过程的影响。实验表明,语音识别和机器翻译等人工智能工具可以有效融入同声传译自主学习过程,并且能对自主学习过程中的自主选材、分组讨论和译文评价等方面产生正向影响。
With rapid development of artificial intelligence technologies, voice recognition and machine translation are becoming increasingly mature and have found their way into English classrooms. The two most basic learning processes involved in simultaneous interpretation course, listening comprehension and immediate interpretation, are also the two aspects most directly affected by voice recognition and machine translation. Therefore, teachers need to make adaptive changes in multiple aspects such as interactive teaching process and teaching outcome assessment. Based on Zimmerman’s model of autonomous learning, this study has integrated artificial intelligence tools including voice recognition and machine translation into autonomous simultaneous interpretation learning mode and tries to discuss their impacts on the process of autonomous simultaneous interpretation learning. The study finds out that artificial intelligence tools can be effectively integrated into the process of autonomous simultaneous interpretation learning, and can have positive impacts on the processes including self-selection of materials, group discussion, and translation evaluation.

References

[1]  冯志伟. 自然语言计算机形式分析的理论与方法[M]. 合肥: 中国科学技术大学出版社, 2017: 19.
[2]  李国红, 姜磊, 张超. 人工智能关键技术专利态势分析[J]. 信息通信技术与政策, 2019(10): 6-9.
[3]  Holec, H. (1981) Autonomy and Foreign Language Learning. Pergamon Press, 41-45.
[4]  Zimmerman, B.J. (1989) A Social Cognitive View of Self-Regulated Academic Learning. Journal of Educational Psychology, 81, 329-339.
https://doi.org/10.1037/0022-0663.81.3.329
[5]  庞维国. 中小学学生自主学习的教学指导模式研究[D]: [博士学位论文]. 上海: 华东师范大学, 2001.
[6]  孙佳林, 郑长龙. 自主学习能力评价的国际研究: 现状、趋势和启示[J]. 比较教育学报, 2021(1): 67-84.
[7]  庞维国. 自主学习理论的新进展[J]. 华东师范大学学报(教育科学版), 1999(3): 68-74.
[8]  陈玉娜, 史晓东. 通过标点恢复提高机器同传效果[J]. 计算机应用, 2020, 40(4): 972-977.
[9]  邓军涛, 许勉军, 赵田园. 人工智能时代的口译技术前沿与口译教育信息化[J]. 外语电化教学, 2021(4): 67-72, 79.
[10]  苏雯超, 李德凤. 技术辅助下新型同声传译的认知负荷与译文质量研究[J]. 外语教学与研究, 2024, 56(1): 125-135, 161.
[11]  Zimmerman, B.J. and Kitsantas, A. (1997) Developmental Phases in Self-Regulation: Shifting from Process Goals to Outcome Goals. Journal of Educational Psychology, 89, 29-36.
https://doi.org/10.1037/0022-0663.89.1.29
[12]  Klein, G. and Hoffman, R. (1993) Seeing the Invisible: Perceptual/Cognitive Aspects of Expertise. In: Rabinowitz, M., Ed., Cognitive Science Foundations of Instruction, Lawrence Erlbaum Associates, 205-217.
[13]  Zimmerman, B.J. (1986) Becoming a Self-Regulated Learner: Which Are the Key Subprocesses? Contemporary Educational Psychology, 11, 307-313.
https://doi.org/10.1016/0361-476x(86)90027-5
[14]  孙海琴, 李可欣, 陆嘉威. 人工智能赋能语音识别与翻译技术对同声传译的影响: 实验与启示[J]. 外语电化教学, 2021(6): 75-80, 86.
[15]  李霄垅, 王梦婕. 基于语音识别APP的同声传译能力培养教学模式建构与研究——以科大讯飞语记APP为例[J]. 外语电化教学, 2018(1): 12-18.

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