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

Towards a better integration of written names for unsupervised speakers identification in videos

International audienceExisting methods for unsupervised identification of speakers in TV broadcast usually rely on the output of a speaker diariza- tion module and try to name each cluster using names provided by another source of information: we call it "late naming". Hence, written names extracted from title blocks tend to lead to high precision identification, although they cannot correct er- rors made during the clustering step. In this paper, we extend our previous "late naming" ap- proach in two ways: "integrated naming" and "early naming". While "late naming" relies on a speaker diarization module op- timized for speaker diarization, "integrated naming" jointly op- timize speaker diarization and name propagation in terms of identification errors. "Early naming" modifies the speaker di- arization module by adding constraints preventing two clusters with different written names to be merged together. While "integrated naming" yields similar identification per- formance as "late naming" (with better precision), "early nam- ing" improves over this baseline both in terms of identification error rate and stability of the clustering stopping criterion

Towards a better integration of written names for unsupervised speakers identification in videos

International audienceExisting methods for unsupervised identification of speakers in TV broadcast usually rely on the output of a speaker diariza- tion module and try to name each cluster using names provided by another source of information: we call it "late naming". Hence, written names extracted from title blocks tend to lead to high precision identification, although they cannot correct er- rors made during the clustering step. In this paper, we extend our previous "late naming" ap- proach in two ways: "integrated naming" and "early naming". While "late naming" relies on a speaker diarization module op- timized for speaker diarization, "integrated naming" jointly op- timize speaker diarization and name propagation in terms of identification errors. "Early naming" modifies the speaker di- arization module by adding constraints preventing two clusters with different written names to be merged together. While "integrated naming" yields similar identification per- formance as "late naming" (with better precision), "early nam- ing" improves over this baseline both in terms of identification error rate and stability of the clustering stopping criterion

Tag Propagation Approaches within Speaking Face Graphs for Multimodal Person Discovery

International audienceThe indexing of broadcast TV archives is a current problem in multimedia research. As the size of these databases grows continuously, meaningful features are needed to describe and connect their elements efficiently, such as the identification of speaking faces. In this context, this paper focuses on two approaches for unsupervised person discovery. Initial tagging of speaking faces is provided by an OCR-based method, and these tags propagate through a graph model based on audiovisual relations between speaking faces. Two propagation methods are proposed, one based on random walks and the other based on a hierarchical approach. To better evaluate their performances, these methods were compared with two graph clustering baselines. We also study the impact of different modality fusions on the graph-based tag propagation scenario. From a quantitative analysis, we observed that the graph propagation techniques always outperform the baselines. Among all compared strategies, the methods based on hierarchical propagation with late fusion and random walk with score-fusion obtained the highest MAP values. Finally, even though these two methods produce highly equivalent results according to Kappa coefficient, the random walk method performs better according to a paired t-test, and the computing time for the hierarchical propagation is more than 4 times lower than the one for the random walk propagation

CRF-Based Context Modeling for Person Identification in Broadcast Videos

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End-to-end learning, and audio-visual human-centric video understanding

The field of machine learning has seen tremendous progress in the last decade, largely due to the advent of deep neural networks. When trained on large-scale labelled datasets, these machine learning algorithms can learn powerful semantic representations directly from the input data, end-to-end. End-to-end learning requires the availability of three core components: useful input data, target outputs, and an objective function for measuring how well the model's predictions match the target outputs. In this thesis, we explore and overcome a series of challenges as related to assembling these three components in the sufficient format and scale for end-to-end learning. The first key idea presented in this thesis is to learn representations by enabling end-to-end learning for tasks where such challenges exist. We first explore whether better representations can be learnt for the image retrieval task by directly optimising the evaluation metric, Average Precision. This is notoriously challenging task, because such rank-based metrics are non-differentiable. We introduce a simple objective function that optimises a smoothed approximation of Average Precision, termed Smooth-AP, and demonstrate the benefits of training end-to-end over prior approaches. Secondly, we explore whether a representation can be learnt end-to-end for the task of image editing, where target data does not exist in sufficient scale. We propose a self-supervised approach that simulates target data by augmenting off-the-shelf image data, giving remarkable benefits over prior work. The second idea presented in this thesis is focused on how to use the rich multi-modal signals that are essential for human perceptual systems as input data for deep neural networks. More specifically, we explore the use of audio-visual input data for the human-centric video understanding task. Here, we first explore if highly optimised speaker verification representations can transfer to the domain of movies where humans intentionally disguise their voice. We do this by collecting an audio-visual dataset of humans speaking in movies. Second, given strong identity discriminating representations, we present two methods that harness the complementarity and redundancy between multi-modal signals in order to build robust perceptual systems for determining who is present in a scene. These methods include an automated pipeline for labelling people in unlabelled video archives, and an approach for clustering people by identity in videos

An audio-visual approach to web video categorization

International audienceIn this paper we address the issue of automatic video genre categorization of web media using an audio-visual approach. To this end, we propose content descriptors which exploit audio, temporal structure and color information. The potential of our descriptors is experimentally validated both from the perspective of a classification system and as an information retrieval approach. Validation is carried out on a real scenario, namely on more than 288 hours of video footage and 26 video genres specific to blip.tv media platform. Additionally, to reduce semantic gap, we propose a new relevance feedback technique which is based on hierarchical clustering. Experimental tests prove that retrieval performance can be significantly increased in this case, becoming comparable to the one obtained with high level semantic textual descriptors

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

Video genre categorization and representation using audio-visual information

International audienceWe propose an audio-visual approach to video genre classification using content descriptors that exploit audio, color, temporal, and contour information. Audio information is extracted at block-level, which has the advantage of capturing local temporal information. At the temporal structure level, we consider action content in relation to human perception. Color perception is quantified using statistics of color distribution, elementary hues, color properties, and relationships between colors. Further, we compute statistics of contour geometry and relationships. The main contribution of our work lies in harnessingn the descriptive power of the combination of these descriptors in genre classification. Validation was carried out on over 91 h of video footage encompassing 7 common video genres, yielding average precision and recall ratios of 87% to 100% and 77% to 100%, respectively, and an overall average correct classification of up to 97%. Also, experimental comparison as part of the MediaEval 2011 benchmarkingn campaign demonstrated the efficiency of the proposed audiovisual descriptors over other existing approaches. Finally, we discuss a 3-D video browsing platform that displays movies using efaturebased coordinates and thus regroups them according to genre