Unsupervised video indexing on audiovisual characterization of persons

Cette thèse consiste à proposer une méthode de caractérisation non-supervisée des intervenants dans les documents audiovisuels, en exploitant des données liées à leur apparence physique et à leur voix. De manière générale, les méthodes d'identification automatique, que ce soit en vidéo ou en audio, nécessitent une quantité importante de connaissances a priori sur le contenu. Dans ce travail, le but est d'étudier les deux modes de façon corrélée et d'exploiter leur propriété respective de manière collaborative et robuste, afin de produire un résultat fiable aussi indépendant que possible de toute connaissance a priori. Plus particulièrement, nous avons étudié les caractéristiques du flux audio et nous avons proposé plusieurs méthodes pour la segmentation et le regroupement en locuteurs que nous avons évaluées dans le cadre d'une campagne d'évaluation. Ensuite, nous avons mené une étude approfondie sur les descripteurs visuels (visage, costume) qui nous ont servis à proposer de nouvelles approches pour la détection, le suivi et le regroupement des personnes. Enfin, le travail s'est focalisé sur la fusion des données audio et vidéo en proposant une approche basée sur le calcul d'une matrice de cooccurrence qui nous a permis d'établir une association entre l'index audio et l'index vidéo et d'effectuer leur correction. Nous pouvons ainsi produire un modèle audiovisuel dynamique des intervenants.This thesis consists to propose a method for an unsupervised characterization of persons within audiovisual documents, by exploring the data related for their physical appearance and their voice. From a general manner, the automatic recognition methods, either in video or audio, need a huge amount of a priori knowledge about their content. In this work, the goal is to study the two modes in a correlated way and to explore their properties in a collaborative and robust way, in order to produce a reliable result as independent as possible from any a priori knowledge. More particularly, we have studied the characteristics of the audio stream and we have proposed many methods for speaker segmentation and clustering and that we have evaluated in a french competition. Then, we have carried a deep study on visual descriptors (face, clothing) that helped us to propose novel approches for detecting, tracking, and clustering of people within the document. Finally, the work was focused on the audiovisual fusion by proposing a method based on computing the cooccurrence matrix that allowed us to establish an association between audio and video indexes, and to correct them. That will enable us to produce a dynamic audiovisual model for each speaker

Unsupervised Speaker Identification in TV Broadcast Based on Written Names

International audienceIdentifying speakers in TV broadcast in an unsuper- vised way (i.e. without biometric models) is a solution for avoiding costly annotations. Existing methods usually use pronounced names, as a source of names, for identifying speech clusters provided by a diarization step but this source is too imprecise for having sufficient confidence. To overcome this issue, another source of names can be used: the names written in a title block in the image track. We first compared these two sources of names on their abilities to provide the name of the speakers in TV broadcast. This study shows that it is more interesting to use written names for their high precision for identifying the current speaker. We also propose two approaches for finding speaker identity based only on names written in the image track. With the "late naming" approach, we propose different propagations of written names onto clusters. Our second proposition, "Early naming", modifies the speaker diarization module (agglomerative clustering) by adding constraints preventing two clusters with different associated written names to be merged together. These methods were tested on the REPERE corpus phase 1, containing 3 hours of annotated videos. Our best "late naming" system reaches an F-measure of 73.1%. "early naming" improves over this result both in terms of identification error rate and of stability of the clustering stopping criterion. By comparison, a mono-modal, supervised speaker identification system with 535 speaker models trained on matching development data and additional TV and radio data only provided a 57.2% F-measure

Combating Fake News with “Reasonable Standards”

Fake news is an intractable concern around the globe, sowing division and distrust in institutions, and undermining election integrity. This Article analyzes the spectrum of private and public regulation of “fake news” from comparative law and normative perspectives. In the United States, combating fake news shares surprising bipartisan support in an ever-divided political landscape. While several proposals have emerged that would strip Internet media companies of the liability shield for third-party content, it is unlikely that they would survive the seemingly insurmountable First Amendment scrutiny. This Article argues for a different tact—an amendment to the Communications Decency Act that addresses platform design choices rather than speech. In doing so, the Article addresses constitutional concerns of online expression and censorship and demonstrates that a “reasonable standard” is consistent with the existing Internet regulatory framework

Democracy and the Media: The Year in C-SPAN Archives Research, Volume 7

Volume 7 of The Year in C-SPAN Archives Research series focuses on the relationship between democracy and the media. Using the extensive collection of the C-SPAN Video Library, chapters cover Trump political rallies, congressional references of late-night comedy, responses of African American congresswomen to COVID-19 bills, and congressional attacks on the media through floor speeches in the House of Representatives and Senate. The C-SPAN Video Library is unique because there is no other research collection that is based on video research of contemporary politics. Methodologically distinctive, much of the research uses new techniques to analyze video, text, and spoken words of political leaders. No other book examines such a wide range of topics―from immigration to climate change to race relations―using video as the basis for research

Political entrepreneurs in social media: Self-monitoring, authenticity and connective democracy. The case of Íñigo Errejón

Political entrepreneurs seek to mobilise public opinion and access large audiences who are not directly interested in politics, but are exposed to the digital environment. The aim of this research was to analyse how these figures promote experimental communication uses on channels far removed from political activity. We focused on Twitch, a successful platform for promoting entertainment and learning in the video games field. To do so, we conducted a significant case study, that of Íñigo Errejón, a Spanish male Member of Parliament, in 2021 through 18 live streamings that lasted 1223 min. We specifically described the conception and use of Twitch, measured the audience’s impact, analysed the accountability exercise through this platform and evaluated the deliberative quality of conversation with users. To conclude, we identified three novel contributions of Twitch to digital political communication: self-monitoring, insofar as the elected politician himself proactively exercises accountability to the public without a third party intervening; the activation of mediated authenticity as a key value in the political actor’s public construction; promoting connective democracy, which would help those sectors not used to employing political information to take an interest in it by detecting attention being paid to their needs and questions