泰米尔语词/字符级识别的深度学习语音合成模型

Sukumar Rajendran, Kiruba Thangam Raja, G. Nagarajan, A. StephenDass, S. Mathivanan, P. Jayagopal
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引用次数: 2

摘要

随着电子产品和社交媒体的日益普及,大量的文本数据以前所未有的速度产生。所有创建的数据都不能被人类读取,他们在自己感兴趣的领域讨论的内容可能会被发现。主题建模是在大量文本中识别主题的一种方法。关于英语主题建模的研究很多。与此同时,全世界有数百万人说泰米尔语;资源稀缺的语言,如全世界数百万人使用的泰米尔语,没有很大的发展。特定深度学习模型的结果通常很难为典型用户解释。他们正在利用各种可视化技术以一种有意义的方式表示深度学习的结果。然后,他们使用相似度、相关性、困惑度和一致性等指标来评估深度学习模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Speech Synthesis Model for Word/Character-Level Recognition in the Tamil Language
As electronics and the increasing popularity of social media are widely used, a large amount of text data is created at unprecedented rates. All data created cannot be read by humans, and what they discuss in their sphere of interest may be found. Modeling of themes is a way to identify subjects in a vast number of texts. There has been a lot of study on subject-modeling in English. At the same time, millions of people worldwide speak Tamil; there is no great development in resource-scarce languages such as Tamil being spoken by millions of people worldwide. The consequences of specific deep learning models are usually difficult to interpret for the typical user. They are utilizing various visualization techniques to represent the outcomes of deep learning in a meaningful way. Then, they use metrics like similarity, correlation, perplexity, and coherence to evaluate the deep learning models.
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