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Document Type

Article

Keywords

Distributed learning, Parallel Computing, Big data, Speech Conversion, ChatGPT

Abstract

Converting text into voice (TTS) has many important uses in fields as diverse as accessibility, instruction, and recreation. As big data continues to expand at a dizzying rate, TTS conversion systems must adapt to new difficulties in terms of data amount and variety. To improve TTS conversion for huge data, we propose here to employ the cutting-edge language model ChatGPT. We begin with a brief history of text-to-speech (TTS) conversion and big data, before discussing the current state of TTS conversion technology and its limitations. We then go on to detail how ChatGPT was built and how it was taught to perform TTS conversion. We conclude by comparing the results of the ChatGPT-based TTS conversion system to those of existing TTS systems and analysing the results on a massive real-world big data dataset. Our experiments show that using ChatGPT improves the quality and efficiency of TTS conversion for huge data by a significant margin.

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