Books on the topic 'Speech Recognition and Transcription Technologies'

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1

Mihelič, France, and Janez Žibert. Speech recognition: Technologies and applications. I-Tech Education and Publishing, 2008.

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2

Kutza, Patricia. Voice recognition: Technologies, markets, opportunities. Business Communications Co., 2002.

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3

Estonia) Baltic Conference on Human Language Technologies (5th 2012 Tartu. Human language technologies: The Baltic perspective : proceedings of the Fifth International Conference Baltic HLT 2012. IOS Press, 2012.

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4

Baltic Conference on Human Language Technologies (2nd 2005 Tallinn, Estonia). The second Baltic Conference on Human Language Technologies: Proceedings, April 4-5, 2005, Tallinn, Estonia. Edited by Langemets Margit and Penjam Priit. Institute of Cybernetics, Tallinn University of Technology, 2005.

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5

Baltic Conference on Human Language Technologies (4th 2010 Rīga, Latvia). Human language technologies: The Baltic perspective : proceedings of the fourth International Conference, Baltic HLT 2010. IOS Press, 2010.

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6

Association canadienne-francaise pour l'avancement des sciences. Congrès. Les techniques d'intelligence artificielle appliquées aux technologies de l'information: Réflexions sur les approches neuroniques, symboliques et numériques appliquées à la vision, l'écrit, la parole et le biomédical : actes du Colloque multidisciplinaire L'intelligence artificielle dans les technologies de l'information tenu dans le cadre du Congrès de l'Acfas à Montréal en mai 1996. ACFAS, 1997.

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7

Rachel, Bowers, Fiscus Jonathan G, and SpringerLink (Online service), eds. Multimodal Technologies for Perception of Humans: International Evaluation Workshops CLEAR 2007 and RT 2007, Baltimore, MD, USA, May 8-11, 2007, Revised Selected Papers. Springer-Verlag Berlin Heidelberg, 2008.

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8

1959-, Dybkjær Laila, Minker Wolfgang, Neumann Heiko, Pieraccini Roberto, Weber Michael, and SpringerLink (Online service), eds. Perception in Multimodal Dialogue Systems: 4th IEEE Tutorial and Research Workshop on Perception and Interactive Technologies for Speech-Based Systems, PIT 2008, Kloster Irsee, Germany, June 16-18, 2008. Proceedings. Springer-Verlag Berlin Heidelberg, 2008.

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9

Lamel, Lori, and Jean-Luc Gauvain. Speech Recognition. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0016.

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Speech recognition is concerned with converting the speech waveform, an acoustic signal, into a sequence of words. Today's approaches are based on a statistical modellization of the speech signal. This article provides an overview of the main topics addressed in speech recognition, which are, acoustic-phonetic modelling, lexical representation, language modelling, decoding, and model adaptation. Language models are used in speech recognition to estimate the probability of word sequences. The main components of a generic speech recognition system are, main knowledge sources, feature analysis, and acoustic and language models, which are estimated in a training phase, and the decoder. The focus of this article is on methods used in state-of-the-art speaker-independent, large-vocabulary continuous speech recognition (LVCSR). Primary application areas for such technology are dictation, spoken language dialogue, and transcription for information archival and retrieval systems. Finally, this article discusses issues and directions of future research.
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10

Keyes, Bettye A. Voice Writing Method - Dragon Professional Individual 16: Mastering Realtime Transcription with Speech Recognition. Voice Writing Method, 2023.

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11

Feasibility Study of Speech Recognition Technologies for Operating within a Medical First Responder's Environment. Storming Media, 2000.

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12

(Editor), Firooz Sadjadi, and Bahram Javidi (Editor), eds. Physics of Automatic Target Recognition (Advanced Sciences and Technologies for Security Applications). Springer, 2007.

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13

Stiefelhagen, Rainer, and John Garofolo. Multimodal Technologies for Perception of Humans: First International Evaluation Workshop on Classification of Events, Activities and Relationships, CLEAR 2006, Southampton, UK, April 6-7, 2006, Revised Selected Papers. Springer London, Limited, 2007.

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14

McCrocklin, Shannon, ed. Technological Resources for Second Language Pronunciation Learning and Teaching. The Rowman & Littlefield Publishing Group., 2022. https://doi.org/10.5040/9781978729483.

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Second language (L2) pronunciation has become increasingly visible as an important area of L2 teaching and research. Despite the growing number of resources available focused on L2 pronunciation, technology in L2 pronunciation has received much less attention. While technology has been an enduring strand of L2 pronunciation research, it has also been somewhat inconspicuous. Indeed, research has examined a wide variety of technologies such as language-learning platforms, speech visualization software, and Automatic Speech Recognition. Despite the abundance of research, it can be difficult to gain a full sense of work in this area given the lack of a comprehensive and consolidated resource or reference. This book endeavors to fill that gap and make L2 pronunciation technologies more visible by providing teachers and researchers an introduction to research in a wide variety of technologies that can support pronunciation learning. While working to introduce practitioners to numerous technologies available, it also dives into the research-basis for their use, providing new studies and data featuring a wide variety of languages and learning contexts.
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15

Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.

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Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic.
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16

Beaven, Tita, and Fernando Rosell-Aguilar, eds. Innovative language pedagogy report. Research-publishing.net, 2021. http://dx.doi.org/10.14705/rpnet.2021.50.9782490057863.

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The Innovative Language Pedagogy Report presents new and emerging approaches to language teaching, learning, and assessment in school, further education, and higher education settings. Researchers and practitioners provide 22 research-informed, short articles on their chosen pedagogy, with examples and resources. The report is jargon-free, written in a readable format, and covers, among others, gamification, open badges, comparative judgement, translanguaging, translation, learning without a teacher, and dialogue facilitation. It also includes technologies such as chatbots, augmented reality, automatic speech recognition, digital corpora, and LMOOCs, as well as pedagogical innovations around virtual exchange, digital storytelling, technology-facilitated oral homework, and TeachMeets.
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