2026-04-17T15:41:17Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4299912025-07-22T18:44:00Zcom_2072_1033col_2072_452951

00925njm 22002777a 4500 dc Serra i Montes, Pol author 2025-02-11 This Master’s Thesis addresses the challenge of Catalan–Castilian code-switching in speech recognition. It develops synthetic corpora by combining machine-translated text chunks, large language model prompts, and semi-structured dialogues, then uses text-to-speech to produce audio samples. After fine-tuning ASR models, results indicate better recognition of artificially generated code-switching data but highlight limitations in real-world scenarios, such as emergency calls. The research underscores the need to incorporate more authentic recordings for stronger generalization, offering a practical framework to improve code-switching handling in multilingual ASR systems. Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic Automatic speech recognition Deep learning (Machine learning) Code switching (Linguistics)) ASR Deep Learning Transformers Code-Switching Reconeixement automàtic de la parla Aprenentatge profund Alternança de codi (Lingüística) Analysis of Catalan-Castilian Code-Switching in Automatic Speech Recognition