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基于语言大模型的智能英语口语学习APP调研与设计研究
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Abstract:
本研究的核心目标是探索和设计一款基于语言大模型的英语口语学习应用程序,旨在解决英语口语应用缺乏针对性陪练的问题。本研究利用了KANO模型作为对目标用户进行使用意向调研,对目前市场上发展较成熟的英语学习产品进行定性和定量分析,基于调研结果,确定APP设计方向重点;同时,研究还涉及了APP的交互外观设计,探讨了结合用户体验的设计原则和方法,以创造出直观、易用且吸引人的用户界面;本研究还介绍了支撑自身应用成型的核心技术——语音转文字技术、大模型对话系统和文字转语音技术等方面。本研究为开发一个创新的、满足用户需求并具有市场竞争力的英语口语学习应用提供了理论依据和实践指南,挖掘了英语口语教育的潜在可能性。
The core objective of this study is to explore and design an English spoken language learning application based on large language models, aimed at addressing the issue of the lack of targeted practice in English speaking applications. This research employed the KANO model to conduct usage intention surveys among the target users and performed both qualitative and quantitative analysis of mature English learning products currently on the market. Based on the survey results, the key focus for the APP design was determined; in addition, the study also involved the investigation of the APP’s interactive exterior design, discussing design principles and methods that integrate user experience to create an intuitive, easy-to-use, and engaging user interface. This study also introduced the core technologies that support the application’s development, such as voice-to-text technology, large model dialogue systems, and text-to-speech technology. This research provides a theoretical foundation and practical guide for the development of an innovative English spoken language learning application that meets user needs and possesses market competitiveness, uncovering the potential possibilities of English spoken language education.
[1] | 邹爽, 何炼锴. 基于Kano模型的数字文旅小程序用户需求及设计策略研究[J]. 新媒体研究, 2023, 9(12): 24-27 33. https://doi.org/10.16604/j.cnki.issn2096-0360.2023.12.004 |
[2] | 刘倩倩. 基于情感化设计理念的高血压管理类APP界面设计研究[D]: [硕士学位论文]. 济南: 山东建筑大学, 2024. |
[3] | 蔡颖. 基于Python的文本数据处理研究[J]. 软件, 2023, 44(5): 179-183. |
[4] | 吴春霞, 冯卓琦. 基于用户体验的社交类APP交互设计应用研究[J]. 鞋类工艺与设计, 2023, 3(22): 114-116. |
[5] | 高龙博. App界面视觉风格设计研究[D]: [硕士学位论文]. 北京: 北京交通大学, 2019. |
[6] | Xue, Z.J., Li, R.R. and Li, M.D. (2022) Recent Progress in Conversational AI. https://arxiv.org/abs/2204.09719 |
[7] | Liu, X.Y., Li, M.D., Chen, L.X., et al. (2021) Asr N-Best Fusion Nets. ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, 6-11 June 2021, 7618-7622. |
[8] | Ni, J., Young, T., Pandelea, V., et al. (2023) Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey. Artificial Intelligence Review, 56, 3055-3155. https://doi.org/10.1007/s10462-022-10248-8 |
[9] | Rownicka, J., Sprenkamp, K., Tripiana, A., Gromoglasov, V. and Kunz, T.P. (2021) Digital Einstein Experience: Fast Text-to-Speech for Conversational AI. https://arxiv.org/abs/2107.10658 |