Relevant bibliographies by topics / Voice recognition software

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Voice recognition software'

Author: Grafiati
Published: 11 December 2022
Last updated: 31 July 2025

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Journal articles on the topic "Voice recognition software"

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Zumalt, Joseph R. "Voice Recognition Technology: Has It Come of Age?" Information Technology and Libraries 24, no. 4 (2005): 180. http://dx.doi.org/10.6017/ital.v24i4.3382.

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<span>Voice recognition software allows computer users to bypass their keyboards and use their voices to enter text. While the library literature is somewhat silent about voice recognition technology, the medical and legal communities have reported some success using it. Voice recognition software was tested for dictation accuracy and usability within an agriculture library at the University of Illinois. Dragon NaturallySpeaking 8.0 was found to be more accurate than speech recognition within Microsoft Office 2003. Helpful Web sites and a short history regarding this breakthrough technol
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Sandilyan, Malarvizhi Babu, and Jonathan Darley. "Voice recognition software: psychiatrist as transcriber." Psychiatrist 37, no. 4 (2013): 130–34. http://dx.doi.org/10.1192/pb.bp.112.038950.

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Aims and methodVoice recognition software is promoted to improve clinician efficiency and decrease overall costs. Our aim was to compare its efficiency against the traditional method of dictation and typing in an older people's community mental health team. We compared the time taken to dictate, edit and type letters, and the total number of days required to send them out after seeing the patient, using the two methods. We also correlated the time taken by one doctor to dictate and edit clinic letters with the actual days on which they were dictated.ResultsThe voice recognition system reduced
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Korn, Kenneth. "Voice Recognition Software for Clinical Use." Journal of the American Academy of Nurse Practitioners 10, no. 11 (1998): 515–18. http://dx.doi.org/10.1111/j.1745-7599.1998.tb00481.x.

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Thompson, Angus. "Voice recognition software and medication errors." Prescriber 35, no. 2 (2024): 29–30. http://dx.doi.org/10.1002/psb.2121.

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Voice recognition software is a useful tool for transcribing medical notes but inaccuracies are common, especially when interpreting drug names. Without the outputs being reviewed by a human familiar with drug names, there is an increased risk of confusion and whilst medication errors are never a laughing matter, the outputs can sometimes be a source of mirth.
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Poulter, Christine. "Voice recognition software—Nuance Dragon naturally speaking." Occupational Medicine 70, no. 1 (2020): 75–76. http://dx.doi.org/10.1093/occmed/kqz128.

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Graham-Cumming, A. "How I Use It: Voice recognition software." Occupational Medicine 62, no. 4 (2012): 315. http://dx.doi.org/10.1093/occmed/kqs031.

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Bodepudi, Anusha, Manjunath Reddy, Swetha Reddy Thodupunori, and Mounika Mandapuram. "Voice Recognition Systems in the Cloud Networks: Has It Reached Its Full Potential?" Asian Journal of Applied Science and Engineering 8, no. 1 (2019): 51–60. http://dx.doi.org/10.18034/ajase.v8i1.11.

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Voice recognition software enables computer users to use keyboards instead of only entering text using their voices. The medical and legal communities have both reported some success with speech recognition technology, even though the library literature is relatively mute on voice recognition technology. Voice-recognition technology can take over typing for someone who cannot do so due to a physical disability. Voice recognition may still be in its infancy, but it is advancing quickly and becoming more accurate, and it is well worth the investment of money, time, and effort required to learn i
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Bodepudi, Anusha, Manjunath Reddy, Sai Srujan Gutlapalli, and Mounika Mandapuram. "Voice Recognition Systems in the Cloud Networks." Asian Journal of Applied Science and Engineering 8, no. 1 (2019): 51–60. http://dx.doi.org/10.18034/ajase.v8i1.12.

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Voice recognition software enables computer users to use keyboards instead of only entering text using their voices. The medical and legal communities have both reported some success with speech recognition technology, even though the library literature is relatively mute on voice recognition technology. Voice-recognition technology can take over typing for someone who cannot do so due to a physical disability. Voice recognition may still be in its infancy, but it is advancing quickly and becoming more accurate, and it is well worth the investment of money, time, and effort required to learn i
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Honeycutt, Lee. "Researching the use of voice recognition writing software." Computers and Composition 20, no. 1 (2003): 77–95. http://dx.doi.org/10.1016/s8755-4615(02)00174-3.

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Bertuca, David J. "Voice recognition software and OCLC: technology that works." OCLC Systems & Services: International digital library perspectives 16, no. 2 (2000): 69–75. http://dx.doi.org/10.1108/10650750010332728.

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Dissertations / Theses on the topic "Voice recognition software"

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D'Silva, Reginald Arthur. "Promoting reading skills of young adult EAL learners through voice recognition software." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/37665.

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The growing international student population in post-secondary institutions in Canada calls for Academic Exchange Programs (AEPs) to focus on promoting reading skills of English as an Additional Language (EAL) students in order to help them read academic and non-academic texts more proficiently. The current study, conducted at a major western Canadian university, investigated the effectiveness of a computer-based software program called the Reading Tutor (RT) in enhancing the reading performance of EAL young adults. A survey determined the reading preferences of participants and reading mater
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Count, Peter. "Utilising voice recognition software to improve reading fluency of struggling adolescent readers." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2016. https://ro.ecu.edu.au/theses/1799.

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Approximately 15-20% of secondary students in Australia experience reading difficulties. For many, the cognitive effort required to decode words or the lack of automaticity in the elements that contribute to fluent reading prevents effective reading comprehension. Because reading comprehension is of critical importance across the curriculum, students with difficulties in this area are at significant academic risk. One effective method of improving reading fluency is ‘repeated readings’ (NICHHD, 2000). The purpose of this study was to examine whether the use of repeated readings delivered via a
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Bruijn, Christina Geertruida de. "Voice quality after dictation to speech recognition software : a perceptual and acoustic study." Thesis, University of Sheffield, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440907.

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Kuffel, Robert F. "Speech recognition software : an alternative to reduce ship control manning /." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Mar%5FKuffel.pdf.

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Thesis (M.S. in Information Systems and Operations)--Naval Postgraduate School, March 2004.<br>Thesis advisor(s): Russell Gottfried, Monique P. Fargues. Includes bibliographical references (p. 43-45). Also available online.
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Garrett, Jennifer Tumlin. "Using Speech Recognition Software to Increase Writing Fluency for Individuals with Physical Disabilities." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/epse_diss/46.

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Writing is an important skill that is necessary throughout school and life. Many students with physical disabilities, however, have difficulty with writing skills due to disability-specific factors, such as motor coordination problems. Due to the difficulties these individuals have with writing, assistive technology is often utilized. One piece of assistive technology, speech recognition software, may help remove the motor demand of writing and help students become more fluent writers. Past research on the use of speech recognition software, however, reveals little information regarding its im
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Roberts, Kelly Drew. "The use of voice recognition software as a compensatory strategy for postsecondary education students receiving services under the category of learning disabled." Thesis, University of Hawaii at Manoa, 2003. http://hdl.handle.net/10125/9024.

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This study expands on the current literature base that investigates the use of voice recognition software (VRS) as a compensatory strategy for written language difficulties often experienced by postsecondary education students receiving services under the category of learning disabled. The current literature base is limited to one study (Higgins & Raskind, 1995) which found that subjects' writing samples, completed with VRS, had higher holistic scores than the samples completed with a transcriber, and without assistance. While these findings are positive many questions remain unanswered. The r
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West, Timothy J. "Implementation and evaluation of commercial off-the-shelf (COTS) voice recognition software as an input device in a windows-type environment." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1996. http://handle.dtic.mil/100.2/ADA311464.

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Thesis (M.S. in Information Technology Management) Naval Postgraduate School, March 1996.<br>"March 1996." Thesis advisor(s): M.P. Fargues, James C. Emery. Includes bibliographical references. Also Available online.
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Mandelman, Gilza Paim. "Transcrição em tempo real de textos utilizando um dicionário fonético." Universidade de Taubaté, 2011. http://www.bdtd.unitau.br/tedesimplificado/tde_busca/arquivo.php?codArquivo=251.

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In the quest to develop a technique which facilitates the process of automatic speech recognition for transcription real-time text using a phonetic dictionary, this work adopts a proposal nicknamed brazilês plus the use of syllables in the transcription process seeking possible imporvements to the automation especially in systems focused on accessibility, or even in helping to interactivity. There was the degree of improvement with the use of our technique especially in the response of the interactive process, decreasing the number of programmable routines, in their own interpretation of sylla
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SILVA, Daniella Dias Cavalcante da. "Reconhecimento de fala contínua para o Português Brasileiro em sistemas embarcados." Universidade Federal de Campina Grande, 2011. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1295.

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Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-07-30T21:22:20Z No. of bitstreams: 1 DANIELLA DIAS CAVALCANTE DA SILVA - TESE PPGEE 2011..pdf: 21267862 bytes, checksum: 34609e6f0c5b3d3d5dbe954562ec3132 (MD5)<br>Made available in DSpace on 2018-07-30T21:22:20Z (GMT). No. of bitstreams: 1 DANIELLA DIAS CAVALCANTE DA SILVA - TESE PPGEE 2011..pdf: 21267862 bytes, checksum: 34609e6f0c5b3d3d5dbe954562ec3132 (MD5) Previous issue date: 2011-12<br>Com o advento da tecnologia, as máquinas predominam em quase todos os cenários do cotidiano das pessoas, sejam essas máquinas computad
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Roberts, Kelly D. "The use of voice recognition software as a compensatory strategy for postsecondary education students receiving services under the category of learning disabled /." 2003. http://hdl.handle.net/10125/9024.

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Books on the topic "Voice recognition software"

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Baumgarten, Alan. Dragon NaturallySpeaking quicktorial: Voice recognition software. South-Western Educational Pub., 2000.

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Rickett, J. Problems with adapting commercial software packages to use voice recognition for command input. Leicester Polytechnic, Centre for Applied Research in Information Systems, School of Mathematics, Computing and Statistics, 1986.

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Scott, Brave, ed. Wired for speech: How voice activates and advances the human-computer relationship. MIT Press, 2005.

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West, Timothy J. Implementation and evaluation of commercial off-the-shelf (COTS) voice recognition software as an input device in a windows-type environment. Naval Postgraduate School, 1996.

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Inc, Cisco Systems, and Cisco Networking Academy Program, eds. Cisco Networking Academy Program fundamentals of voice and data cabling companion guide. Cisco Press, 2003.

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Rutter, Michael, Karl Barksdale, and J. Alan Baumgarten. Dragon Naturally Speaking: Quicktorial: Voice Recognition Software. South-Western Educational Publishing, 1999.

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Parker, Philip M. The 2007-2012 World Outlook for Voice-Recognition Dictation Software. ICON Group International, Inc., 2006.

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Voice Recognition with Software Applications, Student Text with CD-ROM. Career Education, 2000.

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Keyes, Bettye A. Realtime Voice Writing with EclipseVox 11: Realtime Voice Writing Utilizing EclipseVox 11 Speech-Recognition-Based CAT Software. Voice Writing Method, 2023.

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Parker, Philip M. The 2007-2012 Outlook for Voice-Recognition Dictation Software in Japan. ICON Group International, Inc., 2006.

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Book chapters on the topic "Voice recognition software"

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Vojtas, Pavel, Jan Stepan, David Sec, Richard Cimler, and Ondrej Krejcar. "Voice Recognition Software on Embedded Devices." In Intelligent Information and Database Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75417-8_60.

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Klotz, Leonard Georg, Alexander Wünsch, and Mahsa Fischer. "Evaluation of a Voice-Based Emotion Recognition Software in the Psycho-Oncological Care of Cancer Patients." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60405-8_23.

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Kitchenham, Andrew, and Doug Bowes. "Voice/Speech Recognition Software." In Communication Technology for Students in Special Education and Gifted Programs. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-878-1.ch008.

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In this chapter, the authors discuss the promise of speech or voice recognition software and provide practical suggestions for the teacher or any stakeholder working with a disabled child. The authors begin the chapter with a brief overview of the legislation mandating the accommodation of special needs students in the classroom and discuss the implications of assistive technology. The authors then move on to an examination of the promise of the software. The authors end the chapter with practical ideas for implementation should the caregiver believe that voice recognition software will assist
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Madhusudan, D., and Prudhvi Raj Budumuru. "FACE RECOGNITION WITH VOICE APPLICATION." In Artificial Intelligence and Emerging Technologies. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/nbennurch306.

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Facial recognition may be a biometric identification method that is non-intraoperative and suited for many different kinds of applications. It is necessary for implementing the method with a considerable while and appropriate precision while taking hardware timing into account. The application of a machine learning method for instant facial picture recognition is the main focus of this study. The face recognition software employs algorithms to virtually confirm a person's identification by comparing a digital image taken with a camera to a face print that has been stored. One of the key facial
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Nelson, Anne, and Caroline Parker. "Dyslexia and Voice Recognition Software—Really the Perfect Match?" In Contemporary Ergonomics. CRC Press, 2004. http://dx.doi.org/10.1201/9780203494172.ch34.

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Sen, Mrinmoy, Sunanda Jana, Swarnajit Bhattacharya, and Gitika Maity. "AI-Based DBMS Controlled Speech Recognition Model for Some Common Computing Commands." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7524-9.ch008.

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Speech recognition has become an important part of the AI model. It plays a vital role in the correct function of different AI enabled devices that we see today. Many automobile industries use this voice recognition technology in their AI to develop modern voice enabled cars. To that end we have implemented a deep learning-based voice recognition model in the MySQL database to execute various DBMS command automatically through speech recognition by using intelligent Google Speech API and match-LSTM model. Our proposed model accept speech as input and by using intelligent Google Speech API serv
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Happe André, Pouliquen Bruno, Burgun Anita, Cuggia Marc, and Le Beux Pierre. "Combining voice recognition and automatic indexing of medical reports." In Studies in Health Technology and Informatics. IOS Press, 2002. https://doi.org/10.3233/978-1-60750-934-9-382.

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Medical records have been evolving from the traditional paper-based records to digital ones, from the method of dictating reports and transcription to voice recognition systems. The transition to digital operations will not be complete until we have the ability to combine voice recognition with automated indexing of texts. This paper introduces the methods we used to evaluate existing voice recognition software programs and presents NOMINDEX, a system that turns a medical text into MeSH codes, using the French ADM lexical database. Those systems were applied to 28 patient discharge summaries i
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Prakash, V., R. Bhavani, Durga Karthik, D. Rajalakshmi, N. Rajeswari, and M. Martinaa. "Visual Speech Recognition by Lip Reading Using Deep Learning." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1694-8.ch015.

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By using image processing techniques, visual voice recognition (VSR) is able to extract voice or textual data from facial features. Similar to speech recognition systems, lip reading (LR) systems encounter issues because of variations in facial characteristics, speaking rates, skin tones, and pronunciations. An audio speech recognition system can be synchronised with the LR systems. The lip movement data, also known as lip characteristics or visemes, were obtained from the input video clip that was saved in the cloud. It takes each frame's lip features and stores them. Furthermore, training us
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"An Implementation of Voice Control System by Using Cloud Speech Recognition Services." In International Conference on Software Technology and Engineering (ICSTE 2012). ASME Press, 2012. http://dx.doi.org/10.1115/1.860151_ch91.

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Tereikovska, Liudmyla, and Ihor Tereikovskyi. "CONCEPTUAL PRINCIPLES OF NEURONETWORK RECOGNITION OF PHONEMES IN THE VOISE SIGNAL OF DISTANCE LEARNING SYSTEM MEMBERS." In Science, technology and innovation in the modern world. Publishing House “Baltija Publishing”, 2023. http://dx.doi.org/10.30525/978-9934-26-364-4-5.

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One of the most promising ways to improve the effectiveness of distance learning systems is the introduction of interactive educational materials based on the use of voice recognition tools. The introduction of known voice recognition tools into domestic distance learning systems is associated with significant financial costs and is complicated by the need for complex adaptation to the variability of application conditions, which explains the urgency of the task of developing models, methods and tools for recognizing voice signals adapted to the conditions of the distance learning system. The
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Conference papers on the topic "Voice recognition software"

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Moreno-Fernández, Conrado, Fabian Colque-Fernández, José Luis Castillo-Sequera, and Lenis Wong. "System for Automating Medication Orders to the Hospital Pharmacy by Nurses in Peru Through Voice Recognition." In 2025 7th International Conference on Software Engineering and Computer Science (CSECS). IEEE, 2025. https://doi.org/10.1109/csecs64665.2025.11009294.

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Kobayashi, Kazuhiro, and Tomoki Toda. "sprocket: Open-Source Voice Conversion Software." In Odyssey 2018 The Speaker and Language Recognition Workshop. ISCA, 2018. http://dx.doi.org/10.21437/odyssey.2018-29.

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Dittrich, Toby, and Sequoia Star. "Introducing Voice Recognition into Higher Education." In Fourth International Conference on Higher Education Advances. Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/head18.2018.8080.

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Abstract Voice Recognition (VR) software has now evolved to be fast and accurate enough to be useful in many educational settings. This paper describes two new uses for VR technology, both protected by patents, which can effectively address the lack of universal oral training in education today. The first use is Instant Note Capture (INC) which can be employed in live computer presentations and in an online software add-on tool called Incredible Classroom (IC) to place and store voice to text records in educational activities. The second is a new assessment tool called Virtual Oral Recitation
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Black, Dana, Eric J. Rapos, and Matthew Stephan. "Voice-Driven Modeling: Software Modeling Using Automated Speech Recognition." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2019. http://dx.doi.org/10.1109/models-c.2019.00040.

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Yang, Chenghui, Weixin Yang, and Shuwen Wang. "Based on Artificial Neural Networks for voice recognition word segment." In 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN). IEEE, 2011. http://dx.doi.org/10.1109/iccsn.2011.6014920.

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Liu, Yao-He, and Shao-Wen Zou. "Barcode Recognition and Voice Broadcast Based on Distributed System Semantic Search." In 2009 International Conference on Computational Intelligence and Software Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cise.2009.5364807.

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Rattanatamrong, Prapaporn, Onanong Kongmeesub, Tanakorn Dittaporn, et al. "Thai Preschooler Speech Recognition for Voice Enabled Interactive Counting Exercises." In 2022 19th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2022. http://dx.doi.org/10.1109/jcsse54890.2022.9836310.

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Jia, Yanan. "A Deep Learning System for Domain-Specific Speech Recognition." In 8th International Conference on Software Engineering. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131213.

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As human-machine voice interfaces provide easy access to increasingly intelligent machines, many state-of-theart automatic speech recognition (ASR) systems are proposed. However, commercial ASR systems usually have poor performance on domain-specific speech especially under low-resource settings. The author works with pre-trained DeepSpeech2 and Wav2Vec2 acoustic models to develop benefit-specific ASR systems. The domainspecific data are collected using proposed semi-supervised learning annotation with little human intervention. The best performance comes from a fine-tuned Wav2Vec2-Large-LV60
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You, Yurong. "Research on Emotion Recognition in Software Engineering Voice Data Through Deep Learning Techniques." In 2023 2nd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE). IEEE, 2023. http://dx.doi.org/10.1109/cbase60015.2023.10439109.

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Maldonado, Julio Alejandro Villeda, and Jose Arturo Gaona Cuadra. "Natural Language Interface to Database Using the DialogFlow Voice Recognition and Text Conversion API." In 2019 8th International Conference On Software Process Improvement (CIMPS). IEEE, 2019. http://dx.doi.org/10.1109/cimps49236.2019.9082438.

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