A Comparative Analysis of Generative Error Correction Strategies for Long-Form and Noisy Audio - Gonçalves, Yanna Torres; Fadda, Vincenzo Silva; Coelho da Silva, Ticiana L.; Macêdo, José A. Fernandes de (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
Automatic Speech Recognition (ASR) degrades substantially under real-world acoustic conditions, motivating Generative Error Correction (GER) with Large Language Models (LLMs). This study evaluates Single-ASR and Multi-ASR GER, with and without retrieval-augmented generation (RAG) strategies, across 13,500 audio samples under controlled degradation. GER reduces overall Word Error Rate by up to 43.9%, reaching 45.6% under severe degradation, but increases error on clean audio due to over-correction. Maximal Marginal Relevance (MMR) retrieval outperforms random sampling under severe conditions, while Multi-ASR remains competitive but is more conditionsensitive. These results call for adaptive, input-aware correction strategies.