The transcription of parliamentary proceedings is essential for democratic governance. Traditional methods are manual and time-consuming. This work introduces an Automatic Transcription System for the Assembly of the Republic of Portugal (STAAR) that uses an automatic speech recognition model and speaker diarization technologies. STAAR was developed after analyzing existing technologies and the Assembly’s specific needs, leading to an effective solution that integrates with current processes. STAAR stands out for its efficiency in transcribing debates and adapting to parliamentary language nuances. It significantly exceeded expectations by presenting a low transcription error rate, ranging from 1.7 to 11.3%, depending on the context and speech style, reducing the time required to produce the official parliamentary debates journal, and improving overall transcription efficiency. Additionally, STAAR enabled the transcription of previously undocumented parliamentary committee meetings, enhancing the documentation of parliamentary activities. This achievement marks a significant step in modernizing parliamentary processes, increasing transparency and accessibility of political information, and positions the Portuguese Parliament at the forefront of technological innovation in parliamentary debates transcription.

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Automatic transcription,Parliamentary debates,Automatic speech recognition,Natural language processing,Machine learning,Large language model,Speaker diarization

• Ciências da Computação e da Informação - Ciências Naturais
• Línguas e Literaturas - Humanidades