Academic literature on the topic 'Renforcement de la parole'
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Journal articles on the topic "Renforcement de la parole":
Motoi, Ina, Jacinthe Godard, and Emilienne Laforge. "Questionner l’intervention collective? Facilite-elle la participation des citoyennes et des citoyens dans la société par leur positionnement critique?" Revue internationale animation, territoires et pratiques socioculturelles, no. 4 (July 3, 2013): 75–90. http://dx.doi.org/10.55765/atps.i4.232.
Rubinstein, Amnon, and Pascale Zonszain. "« Le renforcement religieux a réveillé le public laïc »." Pardès N°64-65, no. 1 (2019): 191. http://dx.doi.org/10.3917/parde.064.0191.
Germanos, Marie-Aimée. "Fonctions de l’alternance entre arabe standard et vernaculaire libanais et connotations des deux codes dans un discours politique d’opposition." Arabica 65, no. 4 (August 31, 2018): 501–36. http://dx.doi.org/10.1163/15700585-12341501.
Garcia Delahaye, Sylvia, and Caroline Dubath. "Renforcement des liens familiaux dans le cadre de vacances accompagnées : pour un développement des capabilités des mineurs placés et de leurs parents au-delà des situations de pauvreté." Revue française des affaires sociales, no. 3 (December 5, 2023): 131–56. http://dx.doi.org/10.3917/rfas.233.0131.
Quéméner, Myriam. "Entreprises et intelligence artificielle : quels apports, quels risques ?" Sécurité et stratégie 31, no. 3 (March 19, 2024): 54–58. http://dx.doi.org/10.3917/sestr.031.0054.
Cassetta, Michele. "Parole, parole, parole." Dental Cadmos 87, no. 01 (September 2019): 588. http://dx.doi.org/10.19256/d.cadmos.09.2019.08.
Chiari, Alexis. "Parole parole." Feuillets psychanalytiques N° 2, no. 1 (September 21, 2017): 65–77. http://dx.doi.org/10.3917/fpsy.002.0065.
Tyszler, Jean-Jacques. "Parole vide, parole pleine, parole imposée." Journal français de psychiatrie 45, no. 1 (2017): 70. http://dx.doi.org/10.3917/jfp.045.0070.
Raoul, T., J. P. Montigny, and S. Besch. "Renforcement des abdominaux." Journal de Traumatologie du Sport 36, no. 1 (March 2019): 64–71. http://dx.doi.org/10.1016/j.jts.2018.04.005.
Schiavinato, Jacques. "Parole égarée, parole retrouvée." Revue de psychothérapie psychanalytique de groupe 28, no. 1 (1997): 115–27. http://dx.doi.org/10.3406/rppg.1997.1366.
Dissertations / Theses on the topic "Renforcement de la parole":
Floccia, Caroline. "Perception de la parole et apprentissage chez le nouveau-né : étude méthodologique de la procédure de renforcement des succions de haute amplitude." Paris, EHESS, 1996. http://www.theses.fr/1996EHES0128.
Gentet, Enguerrand. "Amélioration de l'intelligibilité de signaux audio de parole en contexte bruité automobile." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT008.
Speech is nowadays present in a number of in-car applications ranging from hands-free communications, radio programs to speech synthesis messages from the various car devices.However, despite the steady car manufacturing progress, significant noise still remains in the car interior that leads to a loss of intelligibility of speech signals. The PhD work aims at developping speech reinforcement tools in order to process the signals before they are played in a noisy in-car environment.A highly effective speech reinforcement approach is to use a frequency equalizer to optimize an intelligibility criterion : the Speech Intelligibility Index (SII). To facilitate optimization, current methods are based on approximations of the criterion. In addition, by concentrating the spectral energy of the signal in areas where the ear is more sensitive, these methods increase the perceived volume which can deteriorate the user experience. Thus, in addition to proposing an exact method of solving the SII maximization problem, our work proposes to introduce and study the influence of a new perceptual constraint in order to maintain the signals at their perceived level.The popularization of machine learning approaches pushes to learn speech reinforcement processings from examples naturally produced in noise (Lombard speech), or by over-articulation (clear speech). Current work fails to achieve intelligibility gains as significant as with natural modification, and we believe that the many temporal aspects neglect may be partially responsible. Our work therefore proposes to deepen these approaches by exploiting learning models and pre-processings adapted to long duration sequences. We also propose a new modeling of the speech rate modifications that directly fits in the machine learning model which had never been done before
Pinault, Florian. "Apprentissage par renforcement pour la généralisation des approches automatiques dans la conception des systèmes de dialogue oral." Phd thesis, Université d'Avignon, 2011. http://tel.archives-ouvertes.fr/tel-00933937.
Khouzaimi, Hatim. "Turn-taking enhancement in spoken dialogue systems with reinforcement learning." Thesis, Avignon, 2016. http://www.theses.fr/2016AVIG0213/document.
Incremental dialogue systems are able to process the user’s speech as it is spoken (without waiting for the end of a sentence before starting to process it). This makes them able to take the floor whenever they decide to (the user can also speak whenever she wants, even if the system is still holding the floor). As a consequence, they are able to perform a richer set of turn-taking behaviours compared to traditional systems. Several contributions are described in this thesis with the aim of showing that dialogue systems’ turn-taking capabilities can be automatically improved from data. First, human-human dialogue is analysed and a new taxonomy of turn-taking phenomena in human conversation is established. Based on this work, the different phenomena are analysed and some of them are selected for replication in a human-machine context (the ones that are more likely to improve a dialogue system’s efficiency). Then, a new architecture for incremental dialogue systems is introduced with the aim of transforming a traditional dialogue system into an incremental one at a low cost (also separating the turn-taking manager from the dialogue manager). To be able to perform the first tests, a simulated environment has been designed and implemented. It is able to replicate user and ASR behaviour that are specific to incremental processing, unlike existing simulators. Combined together, these contributions led to the establishement of a rule-based incremental dialogue strategy that is shown to improve the dialogue efficiency in a task-oriented situation and in simulation. A new reinforcement learning strategy has also been proposed. It is able to autonomously learn optimal turn-taking behavious throughout the interactions. The simulated environment has been used for training and for a first evaluation, where the new data-driven strategy is shown to outperform both the non-incremental and rule-based incremental strategies. In order to validate these results in real dialogue conditions, a prototype through which the users can interact in order to control their smart home has been developed. At the beginning of each interaction, the turn-taking strategy is randomly chosen among the non-incremental, the rule-based incremental and the reinforcement learning strategy (learned in simulation). A corpus of 206 dialogues has been collected. The results show that the reinforcement learning strategy significantly improves the dialogue efficiency without hurting the user experience (slightly improving it, in fact)
Brenon, Alexis. "Modèle profond pour le contrôle vocal adaptatif d'un habitat intelligent." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM057/document.
Smart-homes, resulting of the merger of home-automation, ubiquitous computing and artificial intelligence, support inhabitants in their activity of daily living to improve their quality of life.Allowing dependent and aged people to live at home longer, these homes provide a first answer to society problems as the dependency tied to the aging population.In voice controlled home, the home has to answer to user's requests covering a range of automated actions (lights, blinds, multimedia control, etc.).To achieve this, the control system of the home need to be aware of the context in which a request has been done, but also to know user habits and preferences.Thus, the system must be able to aggregate information from a heterogeneous home-automation sensors network and take the (variable) user behavior into account.The development of smart home control systems is hard due to the huge variability regarding the home topology and the user habits.Furthermore, the whole set of contextual information need to be represented in a common space in order to be able to reason about them and make decisions.To address these problems, we propose to develop a system which updates continuously its model to adapt itself to the user and which uses raw data from the sensors through a graphical representation.This new method is particularly interesting because it does not require any prior inference step to extract the context.Thus, our system uses deep reinforcement learning; a convolutional neural network allowing to extract contextual information and reinforcement learning used for decision-making.Then, this memoir presents two systems, a first one only based on reinforcement learning showing limits of this approach against real environment with thousands of possible states.Introduction of deep learning allowed to develop the second one, ARCADES, which gives good performances proving that this approach is relevant and opening many ways to improve it
Ferreira, Emmanuel. "Apprentissage automatique en ligne pour un dialogue homme-machine situé." Thesis, Avignon, 2015. http://www.theses.fr/2015AVIG0206/document.
A dialogue system should give the machine the ability to interactnaturally and efficiently with humans. In this thesis, we focus on theissue of the development of stochastic dialogue systems. Thus, we especiallyconsider the Partially Observable Markov Decision Process (POMDP)framework which yields state-of-the-art performance on goal-oriented dialoguemanagement tasks. This model enables the system to cope with thecommunication ambiguities due to noisy channel and also to optimize itsdialogue management strategy directly from data with Reinforcement Learning (RL)methods.Considering statistical approaches often requires the availability of alarge amount of training data to reach good performance. However, corpora of interest are seldom readily available and collectingsuch data is both time consuming and expensive. For instance, it mayrequire a working prototype to initiate preliminary experiments with thesupport of expert users or to consider other alternatives such as usersimulation techniques.Very few studies to date have considered learning a dialogue strategyfrom scratch by interacting with real users, yet this solution is ofgreat interest. Indeed, considering the learning process as part of thelife cycle of a system offers a principle framework to dynamically adaptthe system to new conditions in an online and seamless fashion.In this thesis, we endeavour to provide solutions to make possible thisdialogue system cold start (nearly from scratch) but also to improve its ability to adapt to new conditions in operation (domain extension, new user profile, etc.).First, we investigate the conditions under which initial expertknowledge (such as expert rules) can be used to accelerate the policyoptimization of a learning agent. Similarly, we study how polarized userappraisals gathered throughout the course of the interaction can beintegrated into a reinforcement learning-based dialogue manager. Morespecifically, we discuss how this information can be cast intosocially-inspired rewards to speed up the policy optimisation for bothefficient task completion and user adaptation in an online learning setting.The results obtained on a reference task demonstrate that a(quasi-)optimal policy can be learnt in just a few hundred dialogues,but also that the considered additional information is able tosignificantly accelerate the learning as well as improving the noise tolerance.Second, we focus on reducing the development cost of the spoken language understanding module. For this, we exploit recent word embedding models(projection of words in a continuous vector space representing syntacticand semantic properties) to generalize from a limited initial knowledgeabout the dialogue task to enable the machine to instantly understandthe user utterances. We also propose to dynamically enrich thisknowledge with both active learning techniques and state-of-the-artstatistical methods. Our experimental results show that state-of-the-artperformance can be obtained with a very limited amount of in-domain andin-context data. We also show that we are able to refine the proposedmodel by exploiting user returns about the system outputs as well as tooptimize our adaptive learning with an adversarial bandit algorithm tosuccessfully balance the trade-off between user effort and moduleperformance.Finally, we study how the physical embodiment of a dialogue system in a humanoid robot can help the interaction in a dedicated Human-Robotapplication where dialogue system learning and testing are carried outwith real users. Indeed, in this thesis we propose an extension of thepreviously considered decision-making techniques to be able to take intoaccount the robot's awareness of the users' belief (perspective taking)in a RL-based situated dialogue management optimisation procedure
Launay, Michel. "Le renforcement signalé chez l'animal : renforcement positif." Montpellier 3, 1993. http://www.theses.fr/1993MON30014.
The signaled reinforcement is a typical pavlovina conditioning procedure in which the reinforcer is preceded by the presentation of a signal. In instrumental conditioning with signaled reinforcement, the reinforced response lend to the presentation of a stimulus which predicts the reinforcer. Such an experimental paradigm represents an excellent test of the associative processes which develop between responses, signal and reinforcer and, therefore, of the theoretical models describing those processes in animals. The experimental results confirm the validity of recent models of conditioning (e. G. The wagner-rescorla model) as opposed to the traditional s-r interpretations. The results also suggest some constraints the future models should support, especially in relation to the functioning of neural networks or to inferential information processing
Ouni, Slim. "Parole Multimodale : de la parole articulatoire à la parole audiovisuelle." Habilitation à diriger des recherches, Université de Lorraine, 2013. http://tel.archives-ouvertes.fr/tel-00927119.
Forestier, Sébastien. "Intrinsically Motivated Goal Exploration in Child Development and Artificial Intelligence : Learning and Development of Speech and Tool Use." Thesis, Bordeaux, 2019. http://www.theses.fr/2019BORD0247.
Babies and children are curious, active explorers of their world. One of their challenges is to learn of the relations between their actions such as the use of tools or speech, and the changes in their environment. Intrinsic motivations have been little studied in psychology, such that its mechanisms are mostly unknown. On the other hand, most artificial agents and robots have been learning in a way very different from humans. The objective of this thesis is twofold: understanding the role of intrinsic motivations in human development of speech and tool use through robotic modeling, and improving the abilities of artificial agents inspired by the mechanisms of human exploration and learning. A first part of this work concerns the understanding and modeling of intrinsic motivations. We reanalyze a typical tool-use experiment, showing that intrinsically motivated exploration seems to play an important role in the observed behaviors and to interfere with the measured success rates. With a robotic model, we show that an intrinsic motivation based on the learning progress to reach goals with a modular representation can self-organize phases of behaviors in the development of tool-use precursors that share properties with child tool-use development. We present the first robotic model learning both speech and tool use from scratch, which predicts that the grounded exploration of objects in a social interaction scenario should accelerate infant vocal learning of accurate sounds for these objects' names as a result of a goal-directed exploration of the objects. In the second part of this thesis, we extend, formalize and evaluate the algorithms designed to model child development, with the aim to obtain an efficient learning robot. We formalize an approach called Intrinsically Motivated Goal Exploration Processes (IMGEP) that enables the discovery and acquisition of large repertoires of skills. We show within several experimental setups including a real humanoid robot that learning diverse spaces of goals with intrinsic motivations is more efficient for learning complex skills than only trying to directly learn these complex skills
Zimmer, Matthieu. "Apprentissage par renforcement développemental." Thesis, Université de Lorraine, 2018. http://www.theses.fr/2018LORR0008/document.
Reinforcement learning allows an agent to learn a behavior that has never been previously defined by humans. The agent discovers the environment and the different consequences of its actions through its interaction: it learns from its own experience, without having pre-established knowledge of the goals or effects of its actions. This thesis tackles how deep learning can help reinforcement learning to handle continuous spaces and environments with many degrees of freedom in order to solve problems closer to reality. Indeed, neural networks have a good scalability and representativeness. They make possible to approximate functions on continuous spaces and allow a developmental approach, because they require little a priori knowledge on the domain. We seek to reduce the amount of necessary interaction of the agent to achieve acceptable behavior. To do so, we proposed the Neural Fitted Actor-Critic framework that defines several data efficient actor-critic algorithms. We examine how the agent can fully exploit the transitions generated by previous behaviors by integrating off-policy data into the proposed framework. Finally, we study how the agent can learn faster by taking advantage of the development of his body, in particular, by proceeding with a gradual increase in the dimensionality of its sensorimotor space
Books on the topic "Renforcement de la parole":
Fratarcangeli, Fernando. Mina: Parole-- parole-- parole--. Roma: Arcana, 2008.
Pavlovic, Bratislav. Abdos fessiers: Exercices de renforcement musculaire. Paris: Éditions Amphora, 2001.
Tsafack-Nanfosso, Roger A. L'état du renforcement des capacités en Afrique. Harare, Zimbabwe: African Capacity Building Foundation, 2011.
Pozzi, Antonia. Parole. [Milan, Italy]: Garzanti, 2001.
Office, National Audit. Parole. London: Stationery Office, 2000.
Pozzi, Antonia. Parole. Milano: Garzanti, 1989.
Committee, Connecticut General Assembly Legislative Program Review and Investigations. Board of Parole and parole services. Hartford, CT: The Committee, 1993.
Pozzi, Emilio. Parole mbrugliate: Parole vere per Eduardo. Roma: Bulzoni, 2007.
Cattani, Adelino. Come dirlo?: Parole giuste, parole belle. Casoria: Loffredo, 2008.
Cattani, Adelino. Come dirlo?: Parole giuste, parole belle. Casoria: Loffredo, 2008.
Book chapters on the topic "Renforcement de la parole":
Chafaï, Djalil, and Florent Malrieu. "Renforcement." In Recueil de Modèles Aléatoires, 199–213. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49768-5_15.
Levesque, Roger J. R. "Parole." In Encyclopedia of Adolescence, 2036–37. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-1695-2_685.
Antolak-Saper, Natalia. "Parole." In The Role of the Media in Criminal Justice Policy, 110–45. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003220299-5.
Levesque, Roger J. R. "Parole." In Encyclopedia of Adolescence, 2711–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-33228-4_685.
Mitford, Jessica. "Parole." In The American Prison Business, 216–27. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003327424-12.
Gottfredson, Michael R., and Don M. Gottfredson. "Parole Decisions." In Decision Making in Criminal Justice, 229–55. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4757-9954-5_9.
Smith, Alexander B., and Louis Berlin. "Probation and Parole." In Treating the Criminal Offender, 23–54. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4899-2103-1_2.
Prätorius, Rainer. "Eine allgegenwärtige Parole." In Einbindung und Freiraum, 38–71. Wiesbaden: VS Verlag für Sozialwissenschaften, 1989. http://dx.doi.org/10.1007/978-3-663-14442-7_2.
Nietzel, Michael T., and Melissa J. Himelein. "Probation and Parole." In Behavioral Approaches to Crime and Delinquency, 109–33. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-0903-1_4.
Frenz, Dietmar. "Saba, Umberto: Parole." In Kindlers Literatur Lexikon (KLL), 1–2. Stuttgart: J.B. Metzler, 2020. http://dx.doi.org/10.1007/978-3-476-05728-0_17715-1.
Conference papers on the topic "Renforcement de la parole":
Riou, Matthieu, Bassam Jabaian, Stéphane Huet, and Fabrice Lefèvre. "Évaluation de l'adaptation par renforcement d'un générateur en langage naturel neuronal pour le dialogue homme-machine." In XXXIIe Journées d’Études sur la Parole. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/jep.2018-40.
Bin, Qin, Li Pengcheng, Wang Xin, and Zhu Wanli. "Pitch angle control based on renforcement learning." In 2014 26th Chinese Control And Decision Conference (CCDC). IEEE, 2014. http://dx.doi.org/10.1109/ccdc.2014.6852110.
LAMBERT, Serge. "Amélioration et renforcement de sol à l'arrière des quais." In Journées Nationales Génie Côtier - Génie Civil. Editions Paralia, 2014. http://dx.doi.org/10.5150/jngcgc.2014.072.
Perron, Laurence. "Illégalité/illégitimité trans : de la parole fausse à la parole fictive." In Fiducia (I). Crédibilité, confiance, crédit dans les récits de soi. Fabula, 2024. http://dx.doi.org/10.58282/colloques.12302.
Farajzadeh, Fatemeh, Ryan Baylor Killea, Alexander Teytelboym, and Andrew Christopher Trapp. "Optimizing Sponsored Humanitarian Parole." In EAAMO '23: Equity and Access in Algorithms, Mechanisms, and Optimization. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3617694.3623240.
Akbar, Mohammad, and Jean Caelen. "Parole et traduction automatique." In the 17th international conference. Morristown, NJ, USA: Association for Computational Linguistics, 1998. http://dx.doi.org/10.3115/980451.980852.
Akbar, Mohammad, and Jean Caelen. "Parole et traduction automatique." In the 36th annual meeting. Morristown, NJ, USA: Association for Computational Linguistics, 1998. http://dx.doi.org/10.3115/980845.980852.
DUPONT, Guillaume, Alaric ZANIBELLATO, and Nicolas VERJAT. "Renforcement et conception d’ouvrages côtiers avec un matériau responsable : le Geocorail." In Journées Nationales Génie Côtier - Génie Civil. Editions Paralia, 2022. http://dx.doi.org/10.5150/jngcgc.2022.059.
Mahelle, Pierre, and Romain Thuillier. "Renforcement de la sûreté des joints d'arbres des groupes motopompes primaires (GMPP)." In Premières conséquences du REX de Fukushima sur l’exploitation des réacteurs et installations nucléaires. Les Ulis, France: EDP Sciences, 2012. http://dx.doi.org/10.1051/jtsfen/2012pre12.
Shen, Qingfeng, and Ying Zheng. "Langue, Parole and Foreign Language Education." In 2011 International Conference on Management and Service Science (MASS 2011). IEEE, 2011. http://dx.doi.org/10.1109/icmss.2011.5997939.
Reports on the topic "Renforcement de la parole":
Bruce, Judith, Sarah Engebretsen, and Kimberly Glazer. Boîte à Outils: Renforcement des Compétences. Population Council, 2016. http://dx.doi.org/10.31899/pgy9.1065.
DEPARTMENT OF THE ARMY WASHINGTON DC. Boards, Commissions, and Committees: Army Clemency and Parole Board. Fort Belvoir, VA: Defense Technical Information Center, October 1998. http://dx.doi.org/10.21236/ada401997.
Bolton, Laura, and James Georgalakis. Les répercussions socioéconomiques de la Covid-19 dans les pays à revenu faible ou intermédiaire. Institute of Development Studies, October 2022. http://dx.doi.org/10.19088/core.2022.012.
Polinsky, A. Mitchell, and Paul Riskind. Deterrence and the Optimal Use of Prison, Parole, and Probation. Cambridge, MA: National Bureau of Economic Research, May 2017. http://dx.doi.org/10.3386/w23436.
Anwar, Shamena, and Hanming Fang. Testing for Racial Prejudice in the Parole Board Release Process: Theory and Evidence. Cambridge, MA: National Bureau of Economic Research, July 2012. http://dx.doi.org/10.3386/w18239.
Kuziemko, Ilyana. Going Off Parole: How the Elimination of Discretionary Prison Release Affects the Social Cost of Crime. Cambridge, MA: National Bureau of Economic Research, September 2007. http://dx.doi.org/10.3386/w13380.
Park, Walter G., and Douglas Lippoldt. Licences internationales et renforcement des droits de propriété intellectuelle dans les pays en développement. Organisation for Economic Co-Operation and Development (OECD), March 2005. http://dx.doi.org/10.1787/280154758880.
Diop, Nafissatou, Edmond Bagde, Djingri Ouoba, and Molly Melching. Renforcement des capacites villageoises: Comment 23 villages s'initient aux Droits Humains et abandonnent la pratique de l'excision au Burkina Faso. Population Council, 2003. http://dx.doi.org/10.31899/rh2.1006.
Austrian, Karen, and Dennitah Ghati. Conception d'un programme centré sur les filles : Boîte à outils pour l'elaboration, le renforcement et l'expansion de programmes destinés aux adolescentes. Population Council, 2011. http://dx.doi.org/10.31899/pgy12.1052.
Kaboré, Gisele, Abdoulaye Semde, and Lanko Some. Cartographie sociale des espaces de rencontres des adolescentes mariées ou non dans la zone d'intervention du projet. Population Council, 2009. http://dx.doi.org/10.31899/pgy20.1001.