Text-to-Speech Conversion Using Python and Natural Language Processing Techniques

This paper presents a comprehensive Postgraduate-level study integrating traditional and neural Text-to-Speech (TTS) systems with advanced Natural Language Processing (NLP) techniques. The research combines rule-based, concatenative, statistical, and neural TTS approaches with linguistic preprocessing methods such as phoneme mapping, prosody modeling, contextual embedding, and transformer-based sequence modeling. Experimental evaluation using Processing Time, Mean Opinion Score (MOS), and Word Error Rate (WER) demonstrates that NLP-enhanced synthesis significantly improves speech intelligibility and contextual accuracy. The study contributes a scalable Python-based framework suitable for multilingual and assistive applications.