Audio Summarization with Audio Features and Probability Distribution Divergence

International audienceThe automatic summarization of multimedia sources is an important task that facilitates the understanding of an individual by condensing the source while maintaining relevant information. In this paper we focus on audio summarization based on audio features and the probability of distribution divergence. Our method, based on an extractive summarization approach, aims to select the most relevant segments until a time threshold is reached. It takes into account the segment's length, position and informativeness value. Informativeness of each segment is obtained by mapping a set of audio features issued from its Mel-frequency Cepstral Coefficients and their corresponding Jensen-Shannon divergence score. Results over a multi-evaluator scheme shows that our approach provides understandable and informative summaries

Summarizing videos into a target language: Methodology, architectures and evaluation

International audienceThe aim of the work is to report the results of the Chist-Era project AMIS (Access Multilingual Information opinionS). The purpose of AMIS is to answer the following question: How to make the information in a foreign language accessible for everyone? This issue is not limited to translate a source video into a target language video since the objective is to provide only the main idea of an Arabic video in English. This objective necessitates developing research in several areas that are not, all arrived at a maturity state: Video summarization, Speech recognition, Machine translation, Audio summarization and Speech segmentation. In this article we present several possible architectures to achieve our objective, yet we focus on only one of them. The scientific locks are be presented, and we explain how to deal with them. One of the big challenges of this work is to conceive a way to evaluate objectively a system composed of several components knowing that each of them has its limits and can propagate errors through the first component. Also, a subjective evaluation procedure is proposed in which several annotators have been mobilized to test the quality of the achieved summaries

A Fine-grained Multilingual Analysis Based on the Appraisal Theory: Application to Arabic and English Videos

International audienceThe objective of this paper is to compare the opinions of two videos in two different languages. To do so, a fine-grained approach inspired from the appraisal theory is used to analyze the content of the videos that concern the same topic. In general, the methods devoted to sentiment analysis concern the study of the polarity of a text or an utterance. The appraisal approach goes further than the basic polarity sentiments and consider more detailed sentiments by covering additional attributes of opinions such as: Attitude, Graduation and Engagement. In order to achieve such a comparison, in AMIS (Chist-Era project), we collected a corpus of 1503 Arabic and 1874 English videos. These videos need to be aligned in order to compare their contents, that is why we propose several methods to make them comparable. Then the best one is selected to align them and to constitute the data-set necessary for the fine-grained sentiment analysis

A First Summarization System of a Video in a Target Language

International audienceIn this paper, we present the first results of the project AMIS (Access Multilingual Information opinionS) funded by Chist-Era. The main goal of this project is to understand the content of a video in a foreign language. In this work, we consider the understanding process, such as the aptitude to capture the most important ideas contained in a media expressed in a foreign language. In other words, the understanding will be approached by the global meaning of the content of a support and not by the meaning of each fragment of a video. Several stumbling points remain before reaching the fixed goal. They concern the following aspects: Video summarization, Speech recognition, Machine translation and Speech segmentation. All these issues will be discussed and the methods used to develop each of these components will be presented. A first implementation is achieved and each component of this system is evaluated on a representative test data. We propose also a protocol for a global subjective evaluation of AMIS

Extractive Text-Based Summarization of Arabic videos: Issues, Approaches and Evaluations

International audienceIn this paper, we present and evaluate a method for extractive text-based summarization of Arabic videos. The algorithm is proposed in the scope of the AMIS project that aims at helping a user to understand videos given in a foreign language (Arabic). For that, the project proposes several strategies to translate and summarize the videos. One of them consists in transcribing the Ara-bic videos, summarizing the transcriptions, and translating the summary. In this paper we describe the video corpus that was collected from YouTube and present and evaluate the transcription-summarization part of this strategy. Moreover, we present the Automatic Speech Recognition (ASR) system used to transcribe the videos, and show how we adapted this system to the Algerian dialect. Then, we describe how we automatically segment into sentences the sequence of words provided by the ASR system, and how we summarize the obtained sequence of sentences. We evaluate objectively and subjectively our approach. Results show that the ASR system performs well in terms of Word Error Rate on MSA, but needs to be adapted for dealing with Algerian dialect data. The subjective evaluation shows the same behaviour than ASR: transcriptions for videos containing dialectal data were better scored than videos containing only MSA data. However, summaries based on transcriptions are not as well rated, even when transcriptions are better rated. Last, the study shows that features, such as the lengths of transcriptions and summaries, and the subjective score of transcriptions, explain only 31% of the subjective score of summaries

Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.This work has been partially supported by the European Commission ICT COST Action “Multi-task, Multilingual, Multi-modal Language Generation” (CA18231). AE was supported by BAGEP 2021 Award of the Science Academy. EE was supported in part by TUBA GEBIP 2018 Award. BP is in in part funded by Independent Research Fund Denmark (DFF) grant 9063-00077B. IC has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838188. EL is partly funded by Generalitat Valenciana and the Spanish Government throught projects PROMETEU/2018/089 and RTI2018-094649-B-I00, respectively. SMI is partly funded by UNIRI project uniri-drustv-18-20. GB is partly supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Hungarian Artificial Intelligence National Laboratory Programme. COT is partially funded by the Romanian Ministry of European Investments and Projects through the Competitiveness Operational Program (POC) project “HOLOTRAIN” (grant no. 29/221 ap2/07.04.2020, SMIS code: 129077) and by the German Academic Exchange Service (DAAD) through the project “AWAKEN: content-Aware and netWork-Aware faKE News mitigation” (grant no. 91809005). ESA is partially funded by the German Academic Exchange Service (DAAD) through the project “Deep-Learning Anomaly Detection for Human and Automated Users Behavior” (grant no. 91809358)

Combining heterogeneous sources in an interactive multimedia content retrieval model

Interactive multimodal information retrieval systems (IMIR) increase the capabilities of traditional search systems, by adding the ability to retrieve information of different types (modes) and from different sources. This article describes a formal model for interactive multimodal information retrieval. This model includes formal and widespread definitions of each component of an IMIR system. A use case that focuses on information retrieval regarding sports validates the model, by developing a prototype that implements a subset of the features of the model. Adaptive techniques applied to the retrieval functionality of IMIR systems have been defined by analysing past interactions using decision trees, neural networks, and clustering techniques. This model includes a strategy for selecting sources and combining the results obtained from every source. After modifying the strategy of the prototype for selecting sources, the system is reevaluated using classification techniques.This work was partially supported by eGovernAbility-Access project (TIN2014-52665-C2-2-R)

Accessing spoken interaction through dialogue processing [online]

Zusammenfassung Unser Leben, unsere Leistungen und unsere Umgebung, alles wird derzeit durch Schriftsprache dokumentiert. Die rasante Fortentwicklung der technischen Möglichkeiten Audio, Bilder und Video aufzunehmen, abzuspeichern und wiederzugeben kann genutzt werden um die schriftliche Dokumentation von menschlicher Kommunikation, zum Beispiel Meetings, zu unterstützen, zu ergänzen oder gar zu ersetzen. Diese neuen Technologien können uns in die Lage versetzen Information aufzunehmen, die anderweitig verloren gehen, die Kosten der Dokumentation zu senken und hochwertige Dokumente mit audiovisuellem Material anzureichern. Die Indizierung solcher Aufnahmen stellt die Kerntechnologie dar um dieses Potential auszuschöpfen. Diese Arbeit stellt effektive Alternativen zu schlüsselwortbasierten Indizes vor, die Suchraumeinschränkungen bewirken und teilweise mit einfachen Mitteln zu berechnen sind. Die Indizierung von Sprachdokumenten kann auf verschiedenen Ebenen erfolgen: Ein Dokument gehört stilistisch einer bestimmten Datenbasis an, welche durch sehr einfache Merkmale bei hoher Genauigkeit automatisch bestimmt werden kann. Durch diese Art von Klassifikation kann eine Reduktion des Suchraumes um einen Faktor der Größenordnung 4­10 erfolgen. Die Anwendung von thematischen Merkmalen zur Textklassifikation bei einer Nachrichtendatenbank resultiert in einer Reduktion um einen Faktor 18. Da Sprachdokumente sehr lang sein können müssen sie in thematische Segmente unterteilt werden. Ein neuer probabilistischer Ansatz sowie neue Merkmale (Sprecherinitia­ tive und Stil) liefern vergleichbare oder bessere Resultate als traditionelle schlüsselwortbasierte Ansätze. Diese thematische Segmente können durch die vorherrschende Aktivität charakterisiert werden (erzählen, diskutieren, planen, ...), die durch ein neuronales Netz detektiert werden kann. Die Detektionsraten sind allerdings begrenzt da auch Menschen diese Aktivitäten nur ungenau bestimmen. Eine maximale Reduktion des Suchraumes um den Faktor 6 ist bei den verwendeten Daten theoretisch möglich. Eine thematische Klassifikation dieser Segmente wurde ebenfalls auf einer Datenbasis durchgeführt, die Detektionsraten für diesen Index sind jedoch gering. Auf der Ebene der einzelnen Äußerungen können Dialogakte wie Aussagen, Fragen, Rückmeldungen (aha, ach ja, echt?, ...) usw. mit einem diskriminativ trainierten Hidden Markov Model erkannt werden. Dieses Verfahren kann um die Erkennung von kurzen Folgen wie Frage/Antwort­Spielen erweitert werden (Dialogspiele). Dialogakte und ­spiele können eingesetzt werden um Klassifikatoren für globale Sprechstile zu bauen. Ebenso könnte ein Benutzer sich an eine bestimmte Dialogaktsequenz erinnern und versuchen, diese in einer grafischen Repräsentation wiederzufinden. In einer Studie mit sehr pessimistischen Annahmen konnten Benutzer eines aus vier ähnlichen und gleichwahrscheinlichen Gesprächen mit einer Genauigkeit von ~ 43% durch eine graphische Repräsentation von Aktivität bestimmt. Dialogakte könnte in diesem Szenario ebenso nützlich sein, die Benutzerstudie konnte aufgrund der geringen Datenmenge darüber keinen endgültigen Aufschluß geben. Die Studie konnte allerdings für detailierte Basismerkmale wie Formalität und Sprecheridentität keinen Effekt zeigen. Abstract Written language is one of our primary means for documenting our lives, achievements, and environment. Our capabilities to record, store and retrieve audio, still pictures, and video are undergoing a revolution and may support, supplement or even replace written documentation. This technology enables us to record information that would otherwise be lost, lower the cost of documentation and enhance high­quality documents with original audiovisual material. The indexing of the audio material is the key technology to realize those benefits. This work presents effective alternatives to keyword based indices which restrict the search space and may in part be calculated with very limited resources. Indexing speech documents can be done at a various levels: Stylistically a document belongs to a certain database which can be determined automatically with high accuracy using very simple features. The resulting factor in search space reduction is in the order of 4­10 while topic classification yielded a factor of 18 in a news domain. Since documents can be very long they need to be segmented into topical regions. A new probabilistic segmentation framework as well as new features (speaker initiative and style) prove to be very effective compared to traditional keyword based methods. At the topical segment level activities (storytelling, discussing, planning, ...) can be detected using a machine learning approach with limited accuracy; however even human annotators do not annotate them very reliably. A maximum search space reduction factor of 6 is theoretically possible on the databases used. A topical classification of these regions has been attempted on one database, the detection accuracy for that index, however, was very low. At the utterance level dialogue acts such as statements, questions, backchannels (aha, yeah, ...), etc. are being recognized using a novel discriminatively trained HMM procedure. The procedure can be extended to recognize short sequences such as question/answer pairs, so called dialogue games. Dialog acts and games are useful for building classifiers for speaking style. Similarily a user may remember a certain dialog act sequence and may search for it in a graphical representation. In a study with very pessimistic assumptions users are able to pick one out of four similar and equiprobable meetings correctly with an accuracy ~ 43% using graphical activity information. Dialogue acts may be useful in this situation as well but the sample size did not allow to draw final conclusions. However the user study fails to show any effect for detailed basic features such as formality or speaker identity

Inférence de la grammaire structurelle d’une émission TV récurrente à partir du contenu

TV program structuring raises as a major theme in last decade for the task of high quality indexing. In this thesis, we address the problem of unsupervised TV program structuring from the point of view of grammatical inference, i.e., discovering a common structural model shared by a collection of episodes of a recurrent program. Using grammatical inference makes it possible to rely on only minimal domain knowledge. In particular, we assume no prior knowledge on the structural elements that might be present in a recurrent program and very limited knowledge on the program type, e.g., to name structural elements, apart from the recurrence. With this assumption, we propose an unsupervised framework operating in two stages. The first stage aims at determining the structural elements that are relevant to the structure of a program. We address this issue making use of the property of element repetitiveness in recurrent programs, leveraging temporal density analysis to filter out irrelevant events and determine valid elements. Having discovered structural elements, the second stage is to infer a grammar of the program. We explore two inference techniques based either on multiple sequence alignment or on uniform resampling. A model of the structure is derived from the grammars and used to predict the structure of new episodes. Evaluations are performed on a selection of four different types of recurrent programs. Focusing on structural element determination, we analyze the effect on the number of determined structural elements, fixing the threshold applied on the density function as well as the size of collection of episodes. For structural grammar inference, we discuss the quality of the grammars obtained and show that they accurately reflect the structure of the program. We also demonstrate that the models obtained by grammatical inference can accurately predict the structure of unseen episodes, conducting a quantitative and comparative evaluation of the two methods by segmenting the new episodes into their structural components. Finally, considering the limitations of our work, we discuss a number of open issues in structure discovery and propose three new research directions to address in future work.Dans cette thèse, on aborde le problème de structuration des programmes télévisés de manière non supervisée à partir du point de vue de l'inférence grammaticale, focalisant sur la découverte de la structure des programmes récurrents à partir une collection homogène. On vise à découvrir les éléments structuraux qui sont pertinents à la structure du programme, et à l’inférence grammaticale de la structure des programmes. Des expérimentations montrent que l'inférence grammaticale permet de utiliser minimum des connaissances de domaine a priori pour atteindre la découverte de la structure des programmes