Analysis of speaking time and content of the various debates of the presidential campaign : Automated AI analysis of speech time and content of presidential debates based on the audio using speaker detection and topic detection

Abstract: The field of artificial intelligence (AI) has grown rapidly in recent years and its applications are becoming more widespread in various fields, including politics. In particular, presidential debates have become a crucial aspect of election campaigns and it is important to analyze the information exchanged in these debates in an objective way to let voters choose without being influenced by biased data. The objective of this project was to create an automatic analysis tool for presidential debates using AI. The main challenge of the final system was to determine the speaking time of each candidate and to analyze what each candidate said, to detect the topics discussed and to calculate the time spent on each topic. This thesis focus mainly on the speaker detection part of this system. In addition, the high overlap rate in the debates, where candidates cut each other off, posed a significant challenge for speaker diarization, which aims to determine who speaks when. This problem was considered appropriate for a Master’s thesis project, as it involves a combination of advanced techniques in AI and speech processing, making it an important and difficult task. The application to political debates and the accompanying overlapping pathways makes this task both challenging and innovative. There are several ways to solve the problem of speaker detection. We have implemented classical approaches that involve segmentation techniques, speaker representation using embeddings such as i-vectors or x-vectors, and clustering. Yet, due to speech overlaps, the End-to-end solution was implemented using pyannote-audio (an open-source toolkit written in Python for speaker diarization) and the diarization error rate was significantly reduced after refining the model using our own labeled data. The results of this project showed that it was possible to create an automated presidential debate analysis tool using AI. Specifically, this thesis has established a state of the art of speaker detection taking into account the particularities of the politics such as the high speaker overlap rate.