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

Author(s)	Ms. Vaishnavi T
Country	India
Abstract	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.
Keywords	Text-to-Speech, NLP, Neural TTS, Transformer Models, Speech Synthesis, Python, WER, MOS
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-24
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.69755

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International Journal For Multidisciplinary Research