发音评估:传统与现代模式

Ali Babaeian
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摘要

本文讨论了语音在语言能力中的重要性,以及语音在英语使用者有效交际中的作用。它强调了由人为评分的发音评估引起的潜在问题,包括不一致和偏见。为了克服这些挑战,本文探讨了在语音评估中采用人工智能平台。这些平台提供快速的结果,同时保持高效度标准。他们依靠自动语音识别(ASR)和语音分析程序等技术来评估基于重音和语调等超分段特征的发音技能。文章还探讨了人工智能语音评估的未来,这既带来了机遇,也带来了挑战。这些平台提供了可伸缩性、一致性和个性化反馈,增强了学习体验。然而,他们必须解决与评分模型有效性、语音数据多样性、伦理问题以及人与机器反馈的集成相关的问题。总之,在语音评估中采用人工智能正在改变语言测试,在效率和可访问性方面提供优势,同时也提出了与有效性和伦理考虑相关的挑战。持续的研究和开发对于确保人工智能平台满足大规模语言测试中语言学习者和教育工作者不断变化的需求至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pronunciation Assessment: Traditional vs Modern Modes
This article discusses the significance of pronunciation in linguistic competence and its role in effective communication for English speakers. It highlights the potential issues arising from human-rated pronunciation assessments, including inconsistency and bias. To overcome these challenges, the article examines the adoption of AI-powered platforms in pronunciation assessment. These platforms offer rapid results while maintaining high validity standards. They rely on technologies like Automatic Speech Recognition (ASR) and speech analysis programs to evaluate pronunciation skills based on suprasegmental features such as stress and intonation.The article also explores the future of AI-powered pronunciation assessment, which presents both opportunities and challenges. These platforms offer scalability, consistency, and personalized feedback, enhancing the learning experience. However, they must address issues related to scoring model validity, speech data diversity, ethical concerns, and the integration of human and machine feedback. In conclusion, the adoption of AI in pronunciation assessment is transforming language testing, offering advantages in terms of efficiency and accessibility while posing challenges related to validity and ethical considerations. Ongoing research and development will be essential to ensure AI-powered platforms meet the evolving needs of language learners and educators in large-scale language tests.
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