AI Audio Narration With LLM Voice Assignment for Multi-Character Works
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Solution Overview
Problem
Publishing audio versions of written works is expensive and difficult, and existing text-to-speech applications often lack multi-voice narration options, limiting the immersive experience for character-driven works.
Innovation Solution
A system utilizing trained text parsing and classification large language models (LLMs) to parse and classify text data, automatically assign voices based on passage profiles, and generate multi-character audio narration through a graphical user interface (GUI) for customizable audio narration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional text-to-speech applications are used for audio narration, then single-voice narration can be generated, but multi-voice narration capability is lost
Solution Approach 1:
The system segments the narration task by identifying different passage profiles (narrator, character, dialogue) and assigning dedicated voice models to each profile type. This segmentation enables multi-voice narration by dividing the text into distinct segments that can be processed with appropriate voice characteristics.
Solution Approach 2:
The system employs multiple text-to-speech voice models that can serve different functions (narrator voice, character voices, dialogue voices) within a single unified platform. Each voice model is trained on specific datasets to provide specialized narration capabilities while being managed through a common system architecture.
2Reliability
If professional audio narration services are hired, then high-quality multi-character narration can be achieved, but monetary costs and time requirements increase significantly
Solution Approach 1:
The system enables self-service audio narration generation by providing automated passage profile identification and voice assignment capabilities. Users can input text and receive professionally narrated audio without requiring manual intervention from voice actors or audio production specialists, significantly reducing both time and cost while maintaining quality.
Solution Approach 2:
The system replaces the mechanical process of manual audio recording by human voice actors with automated text-to-speech synthesis. Machine learning models analyze passage profiles and generate narration automatically, substituting the traditional human-centric production workflow with an automated computational process that delivers consistent quality without the associated time and cost overhead.
3Adaptability or versatility
If manual voice assignment for each passage is performed, then customized voice assignments can be achieved, but time and operational complexity increase
Solution Approach 1:
The system performs preliminary analysis by automatically identifying passage profiles (narrator, character, dialogue) and pre-assigning appropriate voice models before user review. This preliminary action provides a ready-to-use configuration that maintains customization capability while eliminating the need for users to manually analyze and assign voices to each passage individually.
Solution Approach 2:
The system provides feedback mechanisms that allow users to review automatically assigned voices and make adjustments through a simplified interface. Users can provide feedback on the automated assignments, and the system can learn from this feedback to improve future automatic voice assignments, balancing automation with user control.
Data Source
AI summary
Systems and methods are herein provided for an audio narration system. A method for an audio narration system, comprising: receiving text data; generating, from the text data, parsed text data and related data via a trained text parsing large language model (LLM), wherein the parsed text data comprises a plurality of passages of one or more passage profiles; assigning one or more voices to the plurality of passages; and generating audio data of the parsed text data, wherein the audio data comprises an audio passage for each of the plurality of passages.