Research Paper on Text to Audio Converter using NLP

The development of text-to-speech (TTS) systems has advanced significantly with the introduc on of deep learningbased models. This paper inves gates the impact of various deep learning architectures, such as WaveNet and Tacotron 2, on the naturalness of synthesized speech. By leveraging convolu onal neural networks (CNNs) and recurrent neural networks (RNNs), we explore techniques for improving prosody, intona on, and speech quality. Our experiments show that the integra on of a en on mechanisms and vocoder models leads to more accurate and human-like speech output, par cularly in complex sentence structures. Addi onally, we examine the poten al of TTS systems in mul lingual and emo onal speech synthesis, showing promising results in genera ng speech with diverse accents and emo ons.