Distinct Voices for Enhanced Storytelling Using Deep Learning

Abstract

In recent years, deep learning has revolutionized natural language processing and speech synthesis, enabling machines to narrate text with human-like expression and clarity. This paper presents a novel system titled Distinct Voices for Enhanced Storytelling using Deep Learning, which generates character-specific voice narration for textual stories. The approach combines quotation attribution, character identification, and zero-shot text-to-speech synthesis to automatically assign unique and expressive voices to individual characters in a story. Tools such as BookNLP are used to parse and annotate quotations, while state-of-the-art models like XTTS enable multilingual and emotion-rich voice generation. This enhances listener engagement, particularly in audiobooks and educational contexts, by transforming plain text into immersive, character-driven audio. The system is scalable, requires no manual voice labeling, and demonstrates significant potential in the fields of digital storytelling, accessibility, and human-computer interaction.

Introduction

1. Introduction

Storytelling has evolved from traditional print to digital audio formats such as audiobooks, podcasts, and virtual assistants. However, most narration systems still use a single monotone voice, which limits emotional depth and character diversity.

The proposed solution uses deep learning and natural language processing (NLP) to:

• Attribute dialogue to characters

• Assign distinct, expressive voices to each character

• Generate emotion-rich, multilingual audio narration

This system enhances digital storytelling, making it more engaging, accessible, and personalized.

2. Related Work

A. Quotation Attribution & Character Identification

• BookNLP is a key tool for parsing literary texts and attributing dialogue.

• Contextual embeddings and syntactic cues (e.g., Epure et al., Vishnubhotla et al.) improve attribution accuracy in complex narratives.

B. Voice Cloning & Zero-Shot TTS

• Tools like XTTS and YourTTS allow zero-shot voice synthesis—no voice samples needed.

• These systems support expressive, multilingual speech generation.

C. Multi-Speaker & Expressive TTS

• Models like Tacotron, FastSpeech 2, VALL-E, and MParrotTTS enable high-quality, multi-character, emotion-aware voice synthesis.

3. Methodology

A. System Overview

The pipeline includes:

1. Quotation Attribution

2. Character Identification

3. Voice Assignment

4. TTS Generation

5. Audio Assembly

B. Quotation Detection

• BookNLP detects who is speaking in dialogue.

• Enhanced with contextual and syntactic techniques for greater accuracy.

C. Voice Assignment

• Characters are grouped and assigned synthetic voices based on gender, age, or emotion using XTTS speaker embeddings.

D. Zero-Shot TTS

• XTTS generates realistic, emotional, and multilingual voices without requiring training data for each character.

E. Audio Assembly

• Merges narration and dialogue

• Preserves flow, pacing, and clarity

F. Deployment Interface

• Users can upload stories, preview characters, and download the final audio.

• Suitable for education, entertainment, and assistive technology.

4. Experimental Results

A. Setup

• Data: Text from classic novels like Alice in Wonderland and Sherlock Holmes

• Tools: BookNLP, XTTS v2, Coqui TTS

• Hardware: RTX 3060 GPU, Python-based pipeline

B. Metrics & Results

• Voice quality was rated highly expressive and natural.

• Distinct voices improved listener comprehension and engagement, especially in complex dialogues.

C. User Feedback

• 90% preferred it over traditional single-voice narration.

• Visually impaired users praised clarity and speaker differentiation.

• Children showed higher engagement in educational content.

Conclusion

This paper presents a deep learning-based storytelling system that brings narratives to life by assigning distinct, expressive voices to each character. By integrating advanced natural language processing for speaker attribution with state-of-the-art text-to-speech synthesis using XTTS, the system transforms static literary text into engaging, multi-speaker audio. The experimental results demonstrate high accuracy in character attribution and excellent voice quality, with strong differentiation across characters. Subjective feedback confirmed that listeners found the output more immersive and enjoyable compared to traditional single-voice narration. This approach holds significant promise for a variety of applications including audiobook generation, assistive reading tools, educational storytelling, and interactive fiction. The system not only enhances the storytelling experience but also contributes to accessibility, particularly for visually impaired users. Its scalable, modular design enables it to adapt to different genres, languages, and user preferences, making it a versatile solution in the growing field of AI-generated content.

References

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Copyright

Copyright © 2025 T. Saipriya, Dr. V. Uma Rani, Sunitha Vanamala. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.