The Reform of Pronunciation Teaching in Colleges and Universities by Praat Software From the Perspective of Deep Learning

Khuselt It
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Abstract

Due to the difficulties of speech signal processing, there is still a considerable gap between the ability of machines to correctly process and that of human beings. In order to overcome the defects of isolated learning and noise sensitivity of SOM, this paper proposes a new time self-organization model (TSOM) from the perspective of deep learning. On the basis of self-organizing mapping network, time enhancement mechanism is introduced to improve the system performance. This method makes up for the fixed spatial topology of the original self-organizing mapping network and the neglect of the time factor, which is crucial to the voice signal. At the same time, this paper makes full use of computer-aided technology and rich network resources to provide a comprehensive and systematic English pronunciation learning database and establish learners' pronunciation files. Once learners understand and master the operation of voice analysis software, they can conduct self-assessment and judgment to find out their blind spots and weaknesses in voice acquisition.
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深度学习视角下Praat软件对高校语音教学的改革
由于语音信号处理的困难,机器正确处理语音信号的能力与人类相比还有相当大的差距。为了克服SOM孤立学习和噪声敏感的缺陷,本文从深度学习的角度提出了一种新的时间自组织模型(TSOM)。在自组织映射网络的基础上,引入时间增强机制提高系统性能。该方法弥补了原有自组织映射网络空间拓扑结构固定和忽略了对语音信号至关重要的时间因素的不足。同时,充分利用计算机辅助技术和丰富的网络资源,提供全面系统的英语语音学习数据库,建立学习者的语音档案。学习者一旦了解并掌握了语音分析软件的操作,就可以进行自我评估和判断,找出自己在语音采集方面的盲点和弱点。
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