112 research outputs found
Improving speaker turn embedding by crossmodal transfer learning from face embedding
Learning speaker turn embeddings has shown considerable improvement in
situations where conventional speaker modeling approaches fail. However, this
improvement is relatively limited when compared to the gain observed in face
embedding learning, which has been proven very successful for face verification
and clustering tasks. Assuming that face and voices from the same identities
share some latent properties (like age, gender, ethnicity), we propose three
transfer learning approaches to leverage the knowledge from the face domain
(learned from thousands of images and identities) for tasks in the speaker
domain. These approaches, namely target embedding transfer, relative distance
transfer, and clustering structure transfer, utilize the structure of the
source face embedding space at different granularities to regularize the target
speaker turn embedding space as optimizing terms. Our methods are evaluated on
two public broadcast corpora and yield promising advances over competitive
baselines in verification and audio clustering tasks, especially when dealing
with short speaker utterances. The analysis of the results also gives insight
into characteristics of the embedding spaces and shows their potential
applications
Extraction and Analysis of Dynamic Conversational Networks from TV Series
Identifying and characterizing the dynamics of modern tv series subplots is
an open problem. One way is to study the underlying social network of
interactions between the characters. Standard dynamic network extraction
methods rely on temporal integration, either over the whole considered period,
or as a sequence of several time-slices. However, they turn out to be
inappropriate in the case of tv series, because the scenes shown onscreen
alternatively focus on parallel storylines, and do not necessarily respect a
traditional chronology. In this article, we introduce Narrative Smoothing, a
novel network extraction method taking advantage of the plot properties to
solve some of their limitations. We apply our method to a corpus of 3 popular
series, and compare it to both standard approaches. Narrative smoothing leads
to more relevant observations when it comes to the characterization of the
protagonists and their relationships, confirming its appropriateness to model
the intertwined storylines constituting the plots.Comment: arXiv admin note: substantial text overlap with arXiv:1602.0781
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Speaker diarisation and longitudinal linking in multi-genre broadcast data
This paper presents a multi-stage speaker diarisation system with longitudinal linking developed on BBC multi-genre data for the 2015 Multi-Genre Broadcast (MGB) challenge. The basic speaker diarisation system draws on techniques from the Cambridge March 2005 system with a new deep neural network (DNN)-based speech/non speech segmenter. A newly developed linking stage is next added to the basic diarisation output aiming at the identification of speakers across multiple episodes of the same series. The longitudinal constraint imposes an incremental processing of the episodes, where speaker labels for each episode can be obtained using only material from the episode in question, and those broadcast earlier in time. The nature of the data as well as the longitudinal linking constraint position this diarisation task as a new open-research topic, and a particularly challenging one. Different linking clustering metrics are compared and the lowest within-episode and cross-episode DER scores are achieved on the MGB challenge evaluation set.This work is in part supported by EPSRC Programme Grant EP/I031022/1 (Natural Speech Technology). C. Zhang is also supported by a Cambridge International Scholarship from the Cambridge Commonwealth, European & International Trust.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ASRU.2015.740485
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
A Domain Adaptation Approach to Improve Speaker Turn Embedding Using Face Representation
This paper proposes a novel approach to improve speaker modeling using knowledge transferred from face representation. In particular, we are interested in learning a discriminative metric which allows speaker turns to be compared directly, which is beneficial for tasks such as diarization and dialogue analysis. Our method improves the embedding space of speaker turns by applying maximum mean discrepancy loss to minimize the disparity between the distributions of facial and acoustic embedded features. This approach aims to discover the shared underlying structure of the two embedded spaces, thus enabling the transfer of knowledge from the richer face representation to the counterpart in speech. Experiments are conducted on broadcast TV news datasets, REPERE and ETAPE, to demonstrate the validity of our method. Quantitative results in verification and clustering tasks show promising improvement, especially in cases where speaker turns are short or the training data size is limited
CRF-Based Context Modeling for Person Identification in Broadcast Videos
International audienceno abstrac
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
Exploring TV Seriality and Television Studies through Data-Driven Approaches
The chapter discusses the use of data-driven approaches in television studies, which has become possible due to the increasing availability of digital data. Computational techniques can be used to analyze cultural artifacts, gain insights into audience reactions to specific shows or episodes, and investigate patterns and trends in television programming over time. The chapter also highlights the challenges of analyzing television series, which are complex open systems that interact with external factors such as the production process, audience feedback, and cultural and social context. Content analysis, which involves qualitative and quantitative methods based on the coding process and data collection, can be used to analyze various elements of a TV series.
Generative AI is also discussed, which refers to the use of deep multi-modal algorithms to generate new content such as images, speech, and text. Generative methods like Generative Adversarial Networks (GANs) and Stable Diffusion can create new content that is almost indistinguishable from real data. While generating videos is more challenging, Recurrent Neural Networks (RNNs) like LSTMs can capture the temporal dynamics of the scenes to create interesting and promising applications for complex, but short-duration videos
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