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Deep Learning Speech Synthesis Model for Word/Character-Level Recognition in the Tamil Language

Sukumar Rajendran, Kiruba Thangam Raja, Nagarajan G., Stephen Dass A., Sandeep Kumar M., Prabhu Jayagopal

Source Title: International Journal of e-Collaboration (IJeC)19(4)

ISSN: 1548-3673|EISSN: 1548-3681|EISBN13: 9781668486795|DOI: 10.4018/IJeC.316824

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MLA

Rajendran, Sukumar, et al. "Deep Learning Speech Synthesis Model for Word/Character-Level Recognition in the Tamil Language." IJEC vol.19, no.4 2023: pp.1-14. http://doi.org/10.4018/IJeC.316824

APA

Rajendran, S., Raja, K. T., Nagarajan G., Stephen Dass A., Kumar M., S., & Jayagopal, P. (2023). Deep Learning Speech Synthesis Model for Word/Character-Level Recognition in the Tamil Language. International Journal of e-Collaboration (IJeC), 19(4), 1-14. http://doi.org/10.4018/IJeC.316824

Chicago

Rajendran, Sukumar, et al. "Deep Learning Speech Synthesis Model for Word/Character-Level Recognition in the Tamil Language," International Journal of e-Collaboration (IJeC) 19, no.4: 1-14. http://doi.org/10.4018/IJeC.316824

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Abstract

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.