神经语言-对不同挑战的帮助

L. Srivathsan, R. Srikanth, S. Sivasankaran, M. Chandru
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引用次数: 1

摘要

当今世界有越来越多的残疾人,特别是那些有听力障碍的人,他们在与周围的人以及周围的人交流时面临着一些问题,因为他们需要熟悉他们的手语。目前的技术解决方案包括语音到文字/文本生成器,基于手套(超级手套)的手势图像捕获。但是,这些方法/技术有缺点,特别是在语音到文字/文本生成的情况下,普通人与不同挑战的人交流变得容易,但反过来需要每个人都不熟悉的手语知识,在基于手套的技术的情况下,它需要一个特殊设计的手套,由不同能力的人戴,这成为一个额外的硬件要求。此外,在输入和输出之间也存在延迟。为了克服这些缺点,我们提出了一种利用动态贝叶斯神经系统的解决方案,该系统具有高效的泛化能力,对输入噪声的容忍度高,易于处理高维输出,速度快,不需要并行处理和数学建模。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural speech — An aid for differently challenged
The world today has growing physically challenged people especially those who have hearing-speaking impairment they face several problems in communicating with people around them and also with others around their circle as they need to be familiar with their sign-language. Current technological solutions for this include voice to word/text generator, glove based (super-glove) for hand gesture image capturing. But, these methods/techniques have disadvantages especially in the case of voice to word-/text generation it becomes easy for the common people to communicate with the differently challenged but the reverse requires knowledge of sign-language which everyone will not be familiar with, in the case of glove based technique it requires a specially designed glove to be worn by the differently able this becomes an additional hardware requirement, moreover there is a delay between input and output too. In order to overcome such drawbacks Here we have brought in a solution using Dynamic Bayesian neural systems which has efficient generalization ability, tolerance to input noise, Can handle high dimensional output easily, high speed, parallel processing and mathematical modeling is not required.
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