Adapting ASR Models to Technical Scenarios: A Case Study in the Brazilian Automotive Repair Domain - Silva, Daniel Ribeiro da; Borba, Maria Eduarda Silva; Oliveira, Gustavo dos Reis; Pimenta, Pedro Reis; Silva, Állan Christoffer Pereira; Dutra, Guilherme Correia; Oliveira, Sávio Salvarino Teles de (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL); 2025: Anais do XVI Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana, 2025)
This work proposes a pipeline for adapting automatic speech recognition models to the domain of automotive repair shops in Brazilian Portuguese. The process includes real data collection, manual transcription, and dataset curation, with adjustments to the Conformer, and Wav2Vec 2.0 models, while Whisper model served as a comparative baseline. The approach aims to improve speech recognition accuracy in noisy environments with specific technical vocabulary. The Conformer model achieves the best results, with a word error rate of 12.97 percent and a character error rate of 5.46 percent, surpassing the Whisper Large-v3 in both transcription accuracy and inference speed.