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EMOTIONAL TEXT TO SPEECH SYNTHESIS USING NATURAL LANGUAGE PROCESSING

Authors:

M.Jyothi¹, Bandari Ritesh², Annedla Rithwik Reddy³, Darga Vinay⁴, Darshanam Anusha⁵

^1-5 Department of CSE , TKR College of Engineering & Technology¹

 ¹Assistant Professor, ^2-5B.Tech Students

ABSTRACT: In an era of increasing human-computer interaction, the need for emotionally intelligent systems has become vital. This project presents an Emotion-Aware Text-to-Speech (TTS) system that generates emotionally expressive speech from text inputs using advanced Natural Language Processing (NLP) and deep learning techniques. The system integrates a pre-trained BERT model for emotion classification and the Tacotron 2 architecture for speech synthesis, combined with a vocoder (WaveGlow/Griffin-Lim) to produce natural, high-quality audio.

The pipeline operates in three main stages: text input validation, emotion detection, and emotional speech generation. By accurately detecting emotions such as happiness, sadness, and anger, the system tailors the vocal tone of synthesized speech to reflect the underlying emotional context of the input text. A Flask-based web interface enables real-time interaction, allowing users to enter text, view detected emotion, and play the generated speech output.

The proposed system achieves enhanced emotional realism and clarity compared to traditional TTS methods, making it suitable for applications in virtual assistants, audiobooks, accessibility tools, and education. This project not only demonstrates the effectiveness of combining NLP and deep learning for expressive speech synthesis but also lays the foundation for future improvements in multilingual support, real-time adaptation, and personalized emotional voice generation.

Keywords: Text-to-Speech, Emotion Detection, BERT, Tacotron 2, NLP, Deep Learning, Emotional Speech Synthesis