Littérature scientifique sur le sujet « RECOGNITION APPLICATIONS »
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Articles de revues sur le sujet "RECOGNITION APPLICATIONS"
Begalinova, A., et A. Shintemirov. « EMBEDDED GESTURE RECOGNITION SYSTEM FOR ROBOTIC APPLICATIONS ». Eurasian Journal of Mathematical and Computer Applications 2, no 1 (2014) : 81–89. http://dx.doi.org/10.32523/2306-3172-2014-2-4-81-89.
Texte intégralHamzh, Al Rubaie Evan Madhi. « Text Recognition Applications ». IJARCCE 5, no 10 (30 octobre 2016) : 603–7. http://dx.doi.org/10.17148/ijarcce.2016.510122.
Texte intégralTempleton, Douglas, et Michael Schwenk. « Immunochemical Recognition and Applications ». Pure and Applied Chemistry 86, no 10 (21 octobre 2014) : 1433–34. http://dx.doi.org/10.1515/pac-2014-5053.
Texte intégralSingh, Nilu, R. A. Khan et Raj Shree. « Applications of Speaker Recognition ». Procedia Engineering 38 (2012) : 3122–26. http://dx.doi.org/10.1016/j.proeng.2012.06.363.
Texte intégralL. Almeida, Leandro, Maria S.V. Paiva, Francisco A. Silva et Almir O. Artero. « Super-Resolution Images Enhanced for Applications to Character Recognition ». SIJ Transactions on Computer Science Engineering & ; its Applications (CSEA) 01, no 03 (16 août 2013) : 09–16. http://dx.doi.org/10.9756/sijcsea/v1i3/0103520101.
Texte intégralBusschaert, Nathalie, Claudia Caltagirone, Wim Van Rossom et Philip A. Gale. « Applications of Supramolecular Anion Recognition ». Chemical Reviews 115, no 15 (21 mai 2015) : 8038–155. http://dx.doi.org/10.1021/acs.chemrev.5b00099.
Texte intégralSchlenoff, Craig, Zeid Kootbally, Anthony Pietromartire, Marek Franaszek et Sebti Foufou. « Intention recognition in manufacturing applications ». Robotics and Computer-Integrated Manufacturing 33 (juin 2015) : 29–41. http://dx.doi.org/10.1016/j.rcim.2014.06.007.
Texte intégralRabiner, Lawrence R. « Speech recognition : Technology and applications ». Journal of the Acoustical Society of America 88, S1 (novembre 1990) : S197. http://dx.doi.org/10.1121/1.2028883.
Texte intégralPatil, Anuradha, Chandrashekhar M. Tavade et . « Methods on Real Time Gesture Recognition System ». International Journal of Engineering & ; Technology 7, no 3.12 (20 juillet 2018) : 982. http://dx.doi.org/10.14419/ijet.v7i3.12.17617.
Texte intégralArnav Madan. « Face Recognition using Haar Cascade Classifier ». January 2021 7, no 01 (29 janvier 2021) : 85–87. http://dx.doi.org/10.46501/ijmtst070119.
Texte intégralThèses sur le sujet "RECOGNITION APPLICATIONS"
Olausson, Erik. « Face Recognition for Mobile Phone Applications ». Thesis, Linköping University, Department of Science and Technology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11850.
Texte intégralAtt applicera ansiktsigenkänning direkt på en mobiltelefon är en utmanande uppgift, inte minst med tanke på den begränsade minnes- och processorkapaciteten samt den stora variationen med avseende på ansiktsuttryck, hållning och ljusförhållande i inmatade bilder.
Det är fortfarande långt kvar till ett färdigutvecklat, robust och helautomatiskt ansiktsigenkänningssystem för den här miljön. Men resultaten i det här arbetet visar att genom att plocka ut feature-värden från lokala regioner samt applicera en välgjord warpstrategi för att minska problemen med variationer i position och rotation av huvudet, är det möjligt att uppnå rimliga och användbara igenkänningsnivåer. Speciellt för ett halvautomatiskt system där användaren har sista ordet om vem personen på bilden faktiskt är.
Med ett galleri bestående av 85 personer och endast en referensbild per person nådde systemet en igenkänningsgrad på 60% på en svårklassificerad serie testbilder. Totalt 73% av gångerna var den rätta individen inom de fyra främsta gissningarna.
Att lägga till extra referensbilder till galleriet höjer igenkänningsgraden rejält, till nästan 75% för helt korrekta gissningar och till 83,5% för topp fyra. Detta visar att en strategi där inmatade bilder läggs till som referensbilder i galleriet efterhand som de identifieras skulle löna sig ordentligt och göra systemet bättre efter hand likt en inlärningsprocess.
Detta exjobb belönades med pris för "Bästa industrirelevanta bidrag" vid Svenska sällskapet för automatiserad bildanalys årliga konferens i Lund, 13-14 mars 2008.
Applying face recognition directly on a mobile phone is a challenging proposal due to the unrestrained nature of input images and limitations in memory and processor capabilities.
A robust, fully automatic recognition system for this environment is still a far way off. However, results show that using local feature extraction and a warping scheme to reduce pose variation problems, it is possible to capitalize on high error tolerance and reach reasonable recognition rates, especially for a semi-automatic classification system where the user has the final say.
With a gallery of 85 individuals and only one gallery image per individual available the system is able to recognize close to 60 % of the faces in a very challenging test set, while the correct individual is in the top four guesses 73% of the time.
Adding extra reference images boosts performance to nearly 75% correct recognition and 83.5% in the top four guesses. This suggests a strategy where extra reference images are added one by one after correct classification, mimicking an online learning strategy.
Thompson, J. R. « Applications of pattern recognition in medicine ». Thesis, Open University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377939.
Texte intégralAl-Rajab, Moaath. « Hand gesture recognition for multimedia applications ». Thesis, University of Leeds, 2008. http://etheses.whiterose.ac.uk/607/.
Texte intégralMuller, Neil Leonard. « Image recognition using the Eigenpicture Technique (with specific applications in face recognition and optical character recognition) ». Master's thesis, University of Cape Town, 1998. http://hdl.handle.net/11427/14381.
Texte intégralIn the first part of this dissertation, we present a detailed description of the eigenface technique first proposed by Sirovich and Kirby and subsequently developed by several groups, most notably the Media Lab at MIT. Other significant contributions have been made by Rockefeller University, whose ideas have culminated in a commercial system known as Faceit. For a different techniques (i.e. not eigenfaces) and a detailed comparison of some other techniques, the reader is referred to [5]. Although we followed ideas in the open literature (we believe there that there is a large body of advanced proprietary knowledge, which remains inaccessible), the implementation is our own. In addition, we believe that the method for updating the eigenfaces to deal with badly represented images presented in section 2. 7 is our own. The next stage in this section would be to develop an experimental system that can be extensively tested. At this point however, another, nonscientific difficulty arises, that of developing an adequately large data base. The basic problem is that one needs a training set representative of all faces to be encountered in future. Note that this does not mean that one can only deal with faces in the database, the whole idea is to be able to work with any facial image. However, a data base is only representative if it contains images similar to anything that can be encountered in future. For this reason a representative database may be very large and is not easy to build. In addition for testing purposes one needs multiple images of a large number of people, acquired over a period of time under different physical conditions representing the typical variations encountered in practice. Obviously this is a very slow process. Potentially the variation between the faces in the database can be large suggesting that the representation of all these different images in terms of eigenfaces may not be particularly efficient. One idea is to separate all the facial images into different, more or less homogeneous classes. Again this can only be done with access to a sufficiently large database, probably consisting of several thousand faces.
A, Anila H. « Synthesis of fluorescent probes for specific recognition and imaging applications ». Thesis(Ph.D.), CSIR-National Chemical Laboratory, Pune, 2018. http://dspace.ncl.res.in:8080/xmlui/handle/20.500.12252/4271.
Texte intégralBrown, Georgina. « Considering accent recognition technology for forensic applications ». Thesis, University of York, 2017. http://etheses.whiterose.ac.uk/20393/.
Texte intégralAleixo, Patrícia Nunes. « Object detection and recognition for robotic applications ». Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13811.
Texte intégralThe computer vision assumes an important relevance in the development of robotic applications. In several applications, robots need to use vision to detect objects, a challenging and sometimes difficult task. This thesis is focused on the study and development of algorithms to be used in detection and identification of objects on digital images to be applied on robots that will be used in practice cases. Three problems are addressed: Detection and identification of decorative stones for textile industry; Detection of the ball in robotic soccer; Detection of objects in a service robot, that operates in a domestic environment. In each case, different methods are studied and applied, such as, Template Matching, Hough transform and visual descriptors (like SIFT and SURF). It was chosen the OpenCv library in order to use the data structures to image manipulation, as well as other structures for all information generated by the developed vision systems. Whenever possible, it was used the implementation of the described methods and have been developed new approaches, both in terms of pre-processing algorithms and in terms of modification of the source code in some used functions. Regarding the pre-processing algorithms, were used the Canny edge detector, contours detection, extraction of color information, among others. For the three problems, there are presented and discussed experimental results in order to evaluate the best method to apply in each case. The best method for each application is already integrated or in the process of integration in the described robots.
A visão por computador assume uma importante relevância no desenvolvimento de aplicações robóticas, na medida em que há robôs que precisam de usar a visão para detetar objetos, uma tarefa desafiadora e por vezes difícil. Esta dissertação foca-se no estudo e desenvolvimento de algoritmos para a deteção e identificação de objetos em imagem digital para aplicar em robôs que serão usados em casos práticos. São abordados três problemas: Deteção e identificação de pedras decorativas para a indústria têxtil; Deteção da bola em futebol robótico; Deteção de objetos num robô de serviço, que opera em ambiente doméstico. Para cada caso, diferentes métodos são estudados e aplicados, tais como, Template Matching, transformada de Hough e descritores visuais (como SIFT e SURF). Optou-se pela biblioteca OpenCv com vista a utilizar as suas estruturas de dados para manipulação de imagem, bem como as demais estruturas para toda a informação gerada pelos sistemas de visão desenvolvidos. Sempre que possivel utilizaram-se as implementações dos métodos descritos tendo sido desenvolvidas novas abordagens, quer em termos de algoritmos de preprocessamento quer em termos de alteração do código fonte das funções utilizadas. Como algoritmos de pre-processamento foram utilizados o detetor de arestas Canny, deteção de contornos, extração de informação de cor, entre outros. Para os três problemas, são apresentados e discutidos resultados experimentais, de forma a avaliar o melhor método a aplicar em cada caso. O melhor método em cada aplicação encontra-se já integrado ou em fase de integração dos robôs descritos.
PAOLANTI, MARINA. « Pattern Recognition for challenging Computer Vision Applications ». Doctoral thesis, Università Politecnica delle Marche, 2018. http://hdl.handle.net/11566/252904.
Texte intégralPattern Recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the patterns categories. Nowadays, the application of Pattern Recognition algorithms and techniques is ubiquitous and transversal. With the recent advances in computer vision, we now have the ability to mine such massive visual data to obtain valuable insight about what is happening in the world. The availability of affordable and high resolution sensors (e.g., RGB-D cameras, microphones and scanners) and data sharing have resulted in huge repositories of digitized documents (text, speech, image and video). Starting from such a premise, this thesis addresses the topic of developing next generation Pattern Recognition systems for real applications such as Biology, Retail, Surveillance, Social Media Intelligence and Digital Cultural Heritage. The main goal is to develop computer vision applications in which Pattern Recognition is the key core in their design, starting from general methods, that can be exploited in more fields, and then passing to methods and techniques addressing specific problems. The privileged focus is on up-to-date applications of Pattern Recognition techniques to real-world problems, and on interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods. The final ambition is to spur new research lines, especially within interdisciplinary research scenarios. Faced with many types of data, such as images, biological data and trajectories, a key difficulty was to nd relevant vectorial representations. While this problem had been often handled in an ad-hoc way by domain experts, it has proved useful to learn these representations directly from data, and Machine Learning algorithms, statistical methods and Deep Learning techniques have been particularly successful. The representations are then based on compositions of simple parameterized processing units, the depth coming from the large number of such compositions. It was desirable to develop new, efficient data representation or feature learning/indexing techniques, which can achieve promising performance in the related tasks. The overarching goal of this work consists of presenting a pipeline to select the model that best explains the given observations; nevertheless, it does not prioritize in memory and time complexity when matching models to observations. For the Pattern Recognition system design, the following steps are performed: data collection, features extraction, tailored learning approach and comparative analysis and assessment. The proposed applications open up a wealth of novel and important opportunities for the machine vision community. The newly dataset collected as well as the complex areas taken into exam, make the research challenging. In fact, it is crucial to evaluate the performance of state of the art methods to demonstrate their strength and weakness and help identify future research for designing more robust algorithms. For comprehensive performance evaluation, it is of great importance developing a library and benchmark to gauge the state of the art because the methods design that are tuned to a specic problem do not work properly on other problems. Furthermore, the dataset selection is needed from different application domains in order to offer the user the opportunity to prove the broad validity of methods. Intensive attention has been drawn to the exploration of tailored learning models and algorithms, and their extension to more application areas. The tailored methods, adopted for the development of the proposed applications, have shown to be capable of extracting complex statistical features and efficiently learning their representations, allowing it to generalize well across a wide variety of computer vision tasks, including image classication, text recognition and so on.
Hayes, William S. « Pattern recognition and signal detection in gene finding ». Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/25420.
Texte intégralAbbott, Kevin Toney. « Applications of algebraic geometry to object/image recognition ». [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1935.
Texte intégralLivres sur le sujet "RECOGNITION APPLICATIONS"
Fred, Ana, Maria De Marsico et Mário Figueiredo, dir. Pattern Recognition : Applications and Methods. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27677-9.
Texte intégralFred, Ana, Maria De Marsico et Gabriella Sanniti di Baja, dir. Pattern Recognition Applications and Methods. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53375-9.
Texte intégralBlostein, Dorothea, et Young-Bin Kwon, dir. Graphics Recognition Algorithms and Applications. Berlin, Heidelberg : Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45868-9.
Texte intégralDe Marsico, Maria, Gabriella Sanniti di Baja et Ana Fred, dir. Pattern Recognition Applications and Methods. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05499-1.
Texte intégralDe Marsico, Maria, Gabriella Sanniti di Baja et Ana Fred, dir. Pattern Recognition Applications and Methods. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66125-0.
Texte intégralFred, Ana, et Maria De Marsico, dir. Pattern Recognition Applications and Methods. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12610-4.
Texte intégralDe Marsico, Maria, Gabriella Sanniti di Baja et Ana Fred, dir. Pattern Recognition Applications and Methods. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93647-5.
Texte intégralDevijver, Pierre A., et Josef Kittler, dir. Pattern Recognition Theory and Applications. Berlin, Heidelberg : Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83069-3.
Texte intégralLatorre Carmona, Pedro, J. Salvador Sánchez et Ana L. N. Fred, dir. Pattern Recognition - Applications and Methods. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36530-0.
Texte intégralDe Marsico, Maria, Gabriella Sanniti di Baja et Ana Fred, dir. Pattern Recognition Applications and Methods. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40014-9.
Texte intégralChapitres de livres sur le sujet "RECOGNITION APPLICATIONS"
Huang, Thomas, Ziyou Xiong et Zhenqiu Zhang. « Face Recognition Applications ». Dans Handbook of Face Recognition, 617–38. London : Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-932-1_24.
Texte intégralBerretti, Stefano, Boulbaba Ben Amor, Hassen Drira, Mohamed Daoudi, Anuj Srivastava, Alberto del Bimbo et Pietro Pala. « Applications ». Dans 3D Face Modeling, Analysis and Recognition, 149–202. Solaris South Tower, Singapore : John Wiley & Sons SingaporePte Ltd, 2013. http://dx.doi.org/10.1002/9781118592656.ch5.
Texte intégralCroall, Ian F., et John P. Mason. « Pattern Recognition ». Dans Industrial Applications of Neural Networks, 55–117. Berlin, Heidelberg : Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-84837-7_4.
Texte intégralCristescu, Gabriela, et Liana Lupşa. « Applications in pattern recognition ». Dans Non-Connected Convexities and Applications, 227–46. Boston, MA : Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0003-2_9.
Texte intégralStork, David G., et Marcus E. Hennecke. « Machine Recognition and Applications ». Dans Speechreading by Humans and Machines, 549–55. Berlin, Heidelberg : Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-13015-5_44.
Texte intégralLeca-Bouvier, Béatrice D., et Loïc J. Blum. « Enzyme for Biosensing Applications ». Dans Recognition Receptors in Biosensors, 177–220. New York, NY : Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0919-0_4.
Texte intégralZimmermann, H. J. « Pattern Recognition ». Dans Fuzzy Set Theory — and Its Applications, 217–40. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7949-0_11.
Texte intégralZimmermann, H. J. « Pattern Recognition ». Dans Fuzzy Set Theory — and Its Applications, 187–212. Dordrecht : Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-015-7153-1_11.
Texte intégralKonar, Amit, et Sriparna Saha. « EEG-Gesture Based Artificial Limb Movement for Rehabilitative Applications ». Dans Gesture Recognition, 243–68. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62212-5_8.
Texte intégralTistarelli, Massimo, et Enrico Grosso. « Active Vision-based Face Recognition : Issues, Applications and Techniques ». Dans Face Recognition, 262–86. Berlin, Heidelberg : Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_14.
Texte intégralActes de conférences sur le sujet "RECOGNITION APPLICATIONS"
Miral Kazmi, Syeda. « Hand Gesture Recognition for Sign language ». Dans Human Interaction and Emerging Technologies (IHIET-AI 2022) Artificial Intelligence and Future Applications. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe100925.
Texte intégralK.V., Kiran Kumar, Chetan ., Kiran . et Syed . « Face Recognition using RFID ». Dans International Conference on Computer Applications — Computer Applications - II. Singapore : Research Publishing Services, 2010. http://dx.doi.org/10.3850/978-981-08-7304-2_1389.
Texte intégralLockhart, Jeffrey W., Tony Pulickal et Gary M. Weiss. « Applications of mobile activity recognition ». Dans the 2012 ACM Conference. New York, New York, USA : ACM Press, 2012. http://dx.doi.org/10.1145/2370216.2370441.
Texte intégralWang, Patrick. « Intelligent pattern recognition and applications ». Dans the First ACM International Conference. New York, New York, USA : ACM Press, 2010. http://dx.doi.org/10.1145/1854776.1854782.
Texte intégralRotar, Danut, et Horia Popa Andreescu. « Face Recognition in Automotive Applications ». Dans 2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2018. http://dx.doi.org/10.1109/synasc.2018.00061.
Texte intégralMadeo, Renata C. B., Sarajane M. Peres, Daniel B. Dias et Clodis Boscarioli. « Gesture recognition for fingerspelling applications ». Dans the 12th international ACM SIGACCESS conference. New York, New York, USA : ACM Press, 2010. http://dx.doi.org/10.1145/1878803.1878861.
Texte intégralLee, Jimmy Addison, et Kin Choong Yow. « Image Recognition for Mobile Applications ». Dans 2007 IEEE International Conference on Image Processing. IEEE, 2007. http://dx.doi.org/10.1109/icip.2007.4379550.
Texte intégralNitta, Tsuneo. « Speech recognition applications in Japan ». Dans 3rd International Conference on Spoken Language Processing (ICSLP 1994). ISCA : ISCA, 1994. http://dx.doi.org/10.21437/icslp.1994-170.
Texte intégralVenkataraman, Sashikumar, Milind Sohoni et Gershon Elber. « Blend recognition algorithm and applications ». Dans the sixth ACM symposium. New York, New York, USA : ACM Press, 2001. http://dx.doi.org/10.1145/376957.376970.
Texte intégralKabasakal, Burak, et Emre Sumer. « Gender recognition using innovative pattern recognition techniques ». Dans 2018 26th Signal Processing and Communications Applications Conference (SIU). IEEE, 2018. http://dx.doi.org/10.1109/siu.2018.8404306.
Texte intégralRapports d'organisations sur le sujet "RECOGNITION APPLICATIONS"
Glenn, S. Automated Threat Recognition For Aviation Security Applications. Office of Scientific and Technical Information (OSTI), mars 2020. http://dx.doi.org/10.2172/1643774.
Texte intégralChellappa, R., et S. A. Sirohey. Face recognition technology for law enforcement applications. Gaithersburg, MD : National Institute of Standards and Technology, 1994. http://dx.doi.org/10.6028/nist.ir.5465.
Texte intégralHowell, J. A., G. W. Eccleston, R. Whiteson, H. O. Menlove, C. C. Fuyat, J. K. Halbig, S. F. Klosterbuer et M. F. Mullen. Safeguards applications of pattern recognition and neural networks. Office of Scientific and Technical Information (OSTI), septembre 1993. http://dx.doi.org/10.2172/10102590.
Texte intégralMeteer, Marie, Christopher Barclay et Sean Colbath. Real Time Continuous Speech Recognition for C3I Applications. Fort Belvoir, VA : Defense Technical Information Center, janvier 2001. http://dx.doi.org/10.21236/ada387178.
Texte intégralGlass, James R. Finding Acoustic Regularities in Speech : Applications to Phonetic Recognition. Fort Belvoir, VA : Defense Technical Information Center, décembre 1988. http://dx.doi.org/10.21236/ada207072.
Texte intégralBass, James D. Advancing Noise Robust Automatic Speech Recognition for Command and Control Applications. Fort Belvoir, VA : Defense Technical Information Center, mars 2006. http://dx.doi.org/10.21236/ada461436.
Texte intégralТарасова, Олена Юріївна, et Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.
Texte intégralBilyk, Zhanna I., Yevhenii B. Shapovalov, Viktor B. Shapovalov, Anna P. Megalinska, Fabian Andruszkiewicz et Agnieszka Dołhańczuk-Śródka. Assessment of mobile phone applications feasibility on plant recognition : comparison with Google Lens AR-app. [б. в.], novembre 2020. http://dx.doi.org/10.31812/123456789/4403.
Texte intégralRhody, H. E., J. Hillenbrand et J. A. Biles. Northeast Artificial Intelligence Consortium Annual Report 1986 : Artificial Intelligence Applications to Speech Recognition. Volume 7. Fort Belvoir, VA : Defense Technical Information Center, juin 1988. http://dx.doi.org/10.21236/ada198058.
Texte intégralFatehifar, Mohsen, Josef Schlittenlacher, David Wong et Kevin Munro. Applications Of Automatic Speech Recognition And Text-To-Speech Models To Detect Hearing Loss : A Scoping Review Protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, janvier 2023. http://dx.doi.org/10.37766/inplasy2023.1.0029.
Texte intégral