Web Development
Artificial Intelligence
Linking Digital
Development of Smart Audiobook Generation and AI Character Recognition System
As AI voice and Natural Language Processing (NLP) technologies mature, Linking Digital has initiated the development of the 'Smart Audiobook Generation Platform.' This project uses Generative AI to convert static e-books into vibrant audio content with distinct characters and emotions. By automating text decomposition and character identification, we've broken the bottlenecks of traditional production, leading the publishing industry into the era of AI-powered creation.
Project Type
Digital Transformation
Category
AI Application
Project Background
To enhance the immersion of reading, Linking Digital is committed to upgrading static e-books into emotionally rich audio content. • Development Motivation: Utilizing AI voice technology to transform text into smart audiobooks with natural tones and clear character identities. • Industry Trend: With the rise of Generative AI, traditional publishing must integrate automated dubbing to meet the rapid cycles of the digital market. • Smart Vision: Creating a smart workflow that combines text decomposition, character recognition, and automated dubbing to achieve digital transformation.
Project Challengesand In-Depth Assessment
Traditional audiobook production relies on manual interpretation and dubbing, facing these technical challenges:
01
Large-Scale Text Structuring
Accurately decomposing massive e-book content into chapters, paragraphs, and sentences automatically.
02
Character & Emotion Recognition
Accurately identifying narrators versus character dialogue in Chinese/English and determining corresponding emotions and vocal traits.
03
Extreme Performance Requirements
The system must analyze an entire novel within two minutes, posing a significant challenge to computational efficiency and API integration.
04
Global Operations & Management
The system requires multi-language support, modular dubbing profiles, and cross-regional permission management for global scalability.
Project Resultsand Client Feedback
We devised the following strategies to ensure the precision and stability of AI recognition:
Character-Centric Strategy
Designing sound and emotion at the character level to fundamentally enhance listener immersion and content quality.
Standardized Auto-Production
A complete pipeline from file upload, text decomposition, and emotion analysis to speech synthesis.
High-Performance Parallel Processing
Implementing a parallel computing architecture with multiple ChatGPT APIs. Success thresholds are set at 90% for character recognition and 75% for text decomposition to ensure stable output.
Flexible Modular Design
Reusable character and dubbing modules significantly shorten the production cycle for book series.
Technical Implementation & Framework
Core Development Framework
Built on ASP.NET Core (C#) to ensure system robustness and high-concurrency processing capabilities.
- ASP.NET Core Framework
- Stability & Reliability
- High-Concurrency
AI Semantic Analysis Engine
Integrated with Azure OpenAI for character identification, emotion detection (including age/gender/traits), and semantic analysis.
- Azure OpenAI Integration
- Emotion Detection
- Semantic Analysis
Structured Processing API
Automated bilingual (CN/EN) book decomposition, transforming content into structured data for sentence-by-sentence AI tagging.
- Bilingual Processing
- Content Structuring
- Sentence Tagging
Project Results & Value
Exponential Efficiency Gains
Reduced manual interpretation tasks from weeks to minutes, maximizing content throughput.
Standardized Output Quality
AI-driven logic ensures consistent character emotions, free from human fatigue or subjective bias.
New Opportunities in Smart Publishing
This transformation establishes an internationally competitive AI digital asset platform and audio reading ecosystem.
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