New insights into hierarchical clustering and linguistic normalization for speaker diarization

Face au volume croissant de données audio et multimédia, les technologies liées à l'indexation de données et à l'analyse de contenu ont suscité beaucoup d'intérêt dans la communauté scientifique. Parmi celles-ci, la segmentation et le regroupement en locuteurs, répondant ainsi à la question 'Qui parle quand ?' a émergé comme une technique de pointe dans la communauté de traitement de la parole. D'importants progrès ont été réalisés dans le domaine ces dernières années principalement menés par les évaluations internationales du NIST. Tout au long de ces évaluations, deux approches se sont démarquées : l'une est bottom-up et l'autre top-down. L'ensemble des systèmes les plus performants ces dernières années furent essentiellement des systèmes types bottom-up, cependant nous expliquons dans cette thèse que l'approche top-down comporte elle aussi certains avantages. En effet, dans un premier temps, nous montrons qu'après avoir introduit une nouvelle composante de purification des clusters dans l'approche top-down, nous obtenons des performances comparables à celles de l'approche bottom-up. De plus, en étudiant en détails les deux types d'approches nous montrons que celles-ci se comportent différemment face à la discrimination des locuteurs et la robustesse face à la composante lexicale. Ces différences sont alors exploitées au travers d'un nouveau système combinant les deux approches. Enfin, nous présentons une nouvelle technologie capable de limiter l'influence de la composante lexicale, source potentielle d'artefacts dans le regroupement et la segmentation en locuteurs. Notre nouvelle approche se nomme Phone Adaptive Training par analogie au Speaker Adaptive TrainingThe ever-expanding volume of available audio and multimedia data has elevated technologies related to content indexing and structuring to the forefront of research. Speaker diarization, commonly referred to as the who spoke when?' task, is one such example and has emerged as a prominent, core enabling technology in the wider speech processing research community. Speaker diarization involves the detection of speaker turns within an audio document (segmentation) and the grouping together of all same-speaker segments (clustering). Much progress has been made in the field over recent years partly spearheaded by the NIST Rich Transcription evaluations focus on meeting domain, in the proceedings of which are found two general approaches: top-down and bottom-up. Even though the best performing systems over recent years have all been bottom-up approaches we show in this thesis that the top-down approach is not without significant merit. Indeed we first introduce a new purification component leading to competitive performance to the bottom-up approach. Moreover, while investigating the two diarization approaches more thoroughly we show that they behave differently in discriminating between individual speakers and in normalizing unwanted acoustic variation, i.e.\ that which does not pertain to different speakers. This difference of behaviours leads to a new top-down/bottom-up system combination outperforming the respective baseline system. Finally, we introduce a new technology able to limit the influence of linguistic effects, responsible for biasing the convergence of the diarization system. Our novel approach is referred to as Phone Adaptive Training (PAT).PARIS-Télécom ParisTech (751132302) / SudocSudocFranceF

Improved Video Content Indexing by Multiple Latent Semantic Analysis

Low-level features are now becoming insufficient to build efficient content-based retrieval systems. Users are not interested any longer in retrieving visually similar content, but they expect retrieval systems to also find documents with similar semantic content. Bridging the gap between low-level features and semantic content is a challenging task necessary for future retrieval systems. Latent Semantic Analysis (LSA) was successfully introduced to efficiently index text documents by detecting synonyms and the polysemy of words. We have successfully proposed an adaptation of LSA to model video content for object retrieval and semantic content estimation. Following this idea we now present a new model composed of multiple LSA's (M-LSA) to better represent the video content. In the experimental section, we make a comparison of LSA and M-LSA on two problems, namely object retrieval and semantic content estimation

Rushes video summarization using a collaborative approach

This paper describes the video summarization system developed by the partners of the K-Space European Network of Excellence for the TRECVID 2008 BBC rushes summarization evaluation. We propose an original method based on individual content segmentation and selection tools in a collaborative system. Our system is organized in several steps. First, we segment the video, secondly we identify relevant and redundant segments, and finally, we select a subset of segments to concatenate and build the final summary with video acceleration incorporated. We analyze the performance of our system through the TRECVID evaluation

The MeMAD Submission to the WMT18 Multimodal Translation Task

This paper describes the MeMAD project entry to the WMT Multimodal Machine Translation Shared Task. We propose adapting the Transformer neural machine translation (NMT) architecture to a multi-modal setting. In this paper, we also describe the preliminary experiments with text-only translation systems leading us up to this choice. We have the top scoring system for both English-to-German and English-to-French, according to the automatic metrics for flickr18. Our experiments show that the effect of the visual features in our system is small. Our largest gains come from the quality of the underlying text-only NMT system. We find that appropriate use of additional data is effective.Peer reviewe

A collaborative approach to video summarization

This poster describes an approach to video summarization based on the combination of several decision mechanisms provided by the partners of the KSpace European Network of Excellence. The system has been applied to the TRECVID 2008 BBC rushes summarization task

A Real-Time Life Experience Logging Tool

Abstract. E-memories attempt to digitally encode all life experiences in an archive for later search and real-time recommendation. In this paper we describe a prototype real-time e-memory gathering infrastructure and system, that uses smartphones to gather and organise semantically rich e-memory

IRIM at TRECVID 2011: Semantic Indexing and Instance Search

12 pages - TRECVID workshop notebook papers/slides available at http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.org.htmlInternational audienceThe IRIM group is a consortium of French teams work- ing on Multimedia Indexing and Retrieval. This paper describes its participation to the TRECVID 2011 se- mantic indexing and instance search tasks. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likeli- hood of a video shot to contain a target concept. These scores are then used for producing a ranked list of im- ages or shots that are the most likely to contain the tar- get concept. The pipeline is composed of the following steps: descriptor extraction, descriptor optimization, classification, fusion of descriptor variants, higher-level fusion, and re-ranking. We evaluated a number of dif- ferent descriptors and tried different fusion strategies. The best IRIM run has a Mean Inferred Average Pre- cision of 0.1387, which ranked us 5th out of 19 partic- ipants. For the instance search task, we we used both object based query and frame based query. We formu- lated the query in standard way as comparison of visual signatures either of object with parts of DB frames or as a comparison of visual signatures of query and DB frames. To produce visual signatures we also used two apporaches: the first one is the baseline Bag-Of-Visual- Words (BOVW) model based on SURF interest point descriptor; the second approach is a Bag-Of-Regions (BOR) model that extends the traditional notion of BOVW vocabulary not only to keypoint-based descrip- tors but to region based descriptors