SPARC: An AI-Based Speech Processing and Real-Time Correction System

Abstract

In the world of audio narration and video production, maintaining clear and accurate dialogue is crucial. However, most work done in dubbing mistakes is done in post-production which is often not applicable to live broadcasts. This project aims to develop a real-time voice correction system that automatically detects and corrects speech errors in near real-time while integrating the adjusted audio into ongoing conversations without disrupting the natural flow. This paper utilizes various AI tools like the Nous Hermes 2-Mistral 7B DPO large language model to first generate the reference script for Coqui's XTTS-V2 zero-shot text-to-speech voice cloning model. After the correction is generated, it goes through a series of filters to replace the mistake and seamlessly integrates it. The experiment's user survey demonstrates that the corrected audio is of high quality.