93 research outputs found
Modularity and Neural Integration in Large-Vocabulary Continuous Speech Recognition
This Thesis tackles the problems of modularity in Large-Vocabulary Continuous Speech Recognition with use of Neural Network
CHORUS Deliverable 4.3: Report from CHORUS workshops on national initiatives and metadata
Minutes of the following Workshops:
âą National Initiatives on Multimedia Content Description and Retrieval, Geneva, October 10th, 2007.
âą Metadata in Audio-Visual/Multimedia production and archiving, Munich, IRT, 21st â 22nd November 2007
Workshop in Geneva 10/10/2007
This highly successful workshop was organised in cooperation with the European Commission. The event brought together
the technical, administrative and financial representatives of the various national initiatives, which have been established
recently in some European countries to support research and technical development in the area of audio-visual content
processing, indexing and searching for the next generation Internet using semantic technologies, and which may lead to an
internet-based knowledge infrastructure. The objective of this workshop was to provide a platform for mutual information
and exchange between these initiatives, the European Commission and the participants. Top speakers were present from
each of the national initiatives. There was time for discussions with the audience and amongst the European National
Initiatives. The challenges, communalities, difficulties, targeted/expected impact, success criteria, etc. were tackled. This
workshop addressed how these national initiatives could work together and benefit from each other.
Workshop in Munich 11/21-22/2007
Numerous EU and national research projects are working on the automatic or semi-automatic generation of descriptive and
functional metadata derived from analysing audio-visual content. The owners of AV archives and production facilities are
eagerly awaiting such methods which would help them to better exploit their assets.Hand in hand with the digitization of
analogue archives and the archiving of digital AV material, metadatashould be generated on an as high semantic level as
possible, preferably fully automatically. All users of metadata rely on a certain metadata model. All AV/multimedia search
engines, developed or under current development, would have to respect some compatibility or compliance with the
metadata models in use. The purpose of this workshop is to draw attention to the specific problem of metadata models in the
context of (semi)-automatic multimedia search
Impact of translation on biomedical information extraction from real-life clinical notes
The objective of our study is to determine whether using English tools to
extract and normalize French medical concepts on translations provides
comparable performance to French models trained on a set of annotated French
clinical notes. We compare two methods: a method involving French language
models and a method involving English language models. For the native French
method, the Named Entity Recognition (NER) and normalization steps are
performed separately. For the translated English method, after the first
translation step, we compare a two-step method and a terminology-oriented
method that performs extraction and normalization at the same time. We used
French, English and bilingual annotated datasets to evaluate all steps (NER,
normalization and translation) of our algorithms. Concerning the results, the
native French method performs better than the translated English one with a
global f1 score of 0.51 [0.47;0.55] against 0.39 [0.34;0.44] and 0.38
[0.36;0.40] for the two English methods tested. In conclusion, despite the
recent improvement of the translation models, there is a significant
performance difference between the two approaches in favor of the native French
method which is more efficient on French medical texts, even with few annotated
documents.Comment: 26 pages, 2 figures, 5 table
QCompere @ REPERE 2013
International audienceWe describe QCompere consortium submissions to the REPERE 2013 evaluation campaign. The REPERE challenge aims at gathering four communities (face recognition, speaker identification, optical character recognition and named entity detection) towards the same goal: multimodal person recognition in TV broadcast. First, four mono-modal components are introduced (one for each foregoing community) constituting the elementary building blocks of our various submissions. Then, depending on the target modality (speaker or face recognition) and on the task (supervised or unsupervised recognition), four different fusion techniques are introduced: they can be summarized as propagation-, classifier-, rule- or graph-based approaches. Finally, their performance is evaluated on REPERE 2013 test set and their advantages and limitations are discussed
Web Video in Numbers - An Analysis of Web-Video Metadata
Web video is often used as a source of data in various fields of study. While
specialized subsets of web video, mainly earmarked for dedicated purposes, are
often analyzed in detail, there is little information available about the
properties of web video as a whole. In this paper we present insights gained
from the analysis of the metadata associated with more than 120 million videos
harvested from two popular web video platforms, vimeo and YouTube, in 2016 and
compare their properties with the ones found in commonly used video
collections. This comparison has revealed that existing collections do not (or
no longer) properly reflect the properties of web video "in the wild".Comment: Dataset available from http://download-dbis.dmi.unibas.ch/WWIN
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
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