Relevant bibliographies by topics / Confidence scoring

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

Academic literature on the topic 'Confidence scoring'

Author: Grafiati
Published: 11 December 2022
Last updated: 26 January 2023

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Journal articles on the topic "Confidence scoring"

1

Rippey, Robert M. "A computer program for administering and scoring confidence tests." Behavior Research Methods, Instruments, & Computers 18, no. 1 (1986): 59–60. http://dx.doi.org/10.3758/bf03200999.

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KIM, T. Y. "Bayesian Confidence Scoring and Adaptation Techniques for Speech Recognition." IEICE Transactions on Communications E88-B, no. 4 (2005): 1756–59. http://dx.doi.org/10.1093/ietcom/e88-b.4.1756.

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YOU, Lan. "A Maximum Entropy Model Based Confidence Scoring Algorithm for QA." Journal of Software 16, no. 8 (2005): 1407. http://dx.doi.org/10.1360/jos161407.

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Kamburov, Atanas, Ulrich Stelzl, and Ralf Herwig. "IntScore: a web tool for confidence scoring of biological interactions." Nucleic Acids Research 40, W1 (2012): W140—W146. http://dx.doi.org/10.1093/nar/gks492.

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Jin, Tan, Barley Mak, and Pei Zhou. "Confidence scoring of speaking performance: How does fuzziness become exact?" Language Testing 29, no. 1 (2011): 43–65. http://dx.doi.org/10.1177/0265532211404383.

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Moon, Tae Hee, and So Young Sohn. "Technology scoring model for reflecting evaluator’s perception within confidence limits." European Journal of Operational Research 184, no. 3 (2008): 981–89. http://dx.doi.org/10.1016/j.ejor.2006.11.030.

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Sudoh, Katsuhito, and Mikio Nakano. "Post-dialogue confidence scoring for unsupervised statistical language model training." Speech Communication 45, no. 4 (2005): 387–400. http://dx.doi.org/10.1016/j.specom.2004.10.017.

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Hazen, Timothy J., Stephanie Seneff, and Joseph Polifroni. "Recognition confidence scoring and its use in speech understanding systems." Computer Speech & Language 16, no. 1 (2002): 49–67. http://dx.doi.org/10.1006/csla.2001.0183.

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Zavodszky, Maria I., Andrew W. Stumpff-Kane, David J. Lee, and Michael Feig. "Scoring confidence index: statistical evaluation of ligand binding mode predictions." Journal of Computer-Aided Molecular Design 23, no. 5 (2009): 289–99. http://dx.doi.org/10.1007/s10822-008-9258-8.

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Jeong, Yongwon, and Hyung Soon Kim. "Recognition confidence scoring using recognition results from perturbed input feature vectors." Electronics Letters 37, no. 18 (2001): 1143. http://dx.doi.org/10.1049/el:20010776.

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Dissertations / Theses on the topic "Confidence scoring"

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Kamppari, Simo O. (Simo Olli) 1976. "Word and phone level acoustic confidence scoring for speech understanding systems." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86458.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (p. 89-91).<br>by Simo O. Kamppari.<br>M.Eng.
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Thambiratnam, Albert J. K. "Acoustic keyword spotting in speech with applications to data mining." Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/37254/1/Albert_Thambiratnam_Thesis.pdf.

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Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spotting to data mining tasks. The thesis makes a number of major contributions to the ¯eld of keyword spotting. The ¯rst major contribution is the development of a novel keyword veri¯cation method named Cohort Word Veri¯cation. This method combines high level lin- guistic information with cohort-based veri¯cation techniques to obtain dramatic improvements in veri¯cation performance, in particular for the problematic short duration target word class. The second major contribution is the development of a novel audio document indexing technique named Dynamic Match Lattice Spotting. This technique aug- ments lattice-based audio indexing principles with dynamic sequence matching techniques to provide robustness to erroneous lattice realisations. The resulting algorithm obtains signi¯cant improvement in detection rate over lattice-based audio document indexing while still maintaining extremely fast search speeds. The third major contribution is the study of multiple veri¯er fusion for the task of keyword veri¯cation. The reported experiments demonstrate that substantial improvements in veri¯cation performance can be obtained through the fusion of multiple keyword veri¯ers. The research focuses on combinations of speech background model based veri¯ers and cohort word veri¯ers. The ¯nal major contribution is a comprehensive study of the e®ects of limited training data for keyword spotting. This study is performed with consideration as to how these e®ects impact the immediate development and deployment of speech technologies for non-English languages.
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Wang, Rung-Yu, and 王榕榆. "Development of an On-Line Assessment System based on a Confident Scoring Mechanism." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/13768398507523597609.

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碩士<br>國立暨南國際大學<br>資訊管理學系<br>94<br>In a conventional in-class course, the teacher needs to face several students simultaneously, and hence it is almost impossible to record the leaning portfolio for each student, which makes it difficulty to analyze individual learning status. Moreover, conventional paper-and-pencil test is mainly suitable to evaluate the learning achievement of a student. To diagnose the learning problem of individual student, more information is needed. The advance of network and computer technologies offers a new channel to cope with these problems. Via collecting the online learning and testing behaviors of students, each student’s learning status can be obtained, and learning suggestion for individual student can be provided. In addition, the teacher can know the status of each student well. In this study, we attempt to develop a new evaluation model based on the student’s confidence degree of answering the test item by taking ‘time to answer the item’ into consideration. We expect the new model can be used to more accurately identify the learning problem of each student, such that individual learning guidance and suggestion can be given. To evaluate our novel approach, an experiment has been conducted on the Chinese Word Identification course. Experimental results showed that our approach can provide more accurately learning suggestions to students, and hence is more helpful in improving their learning performance than previously proposed approach.
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Books on the topic "Confidence scoring"

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Borgatti, Roberto. A Swing You Can Trust: A Breakthrough Approach for Confident, Low-Scoring Play. Atria, 2007.

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Kanar, Carol C. Confident Student 5th Edition, Plus Meyers Briggs Type Indicator And Retention Management System Test Booklet Brief Form B With Answer Sheet And Scoring. 5th ed. Houghton Mifflin Company, 2003.

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Book chapters on the topic "Confidence scoring"

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Jin, Tan. "Putting Rater Confidence in Its Place: A Qualitative Investigation of Raters’ Perceptions on Using Confidence Scoring in Speaking Tests." In English Language Education and Assessment. Springer Singapore, 2014. http://dx.doi.org/10.1007/978-981-287-071-1_12.

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"Proteomics Standards Initiative Confidence Scoring System." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_101244.

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Marques, António José Pereira Silva, Helena Maria Martins Caldas, Mariana Castro Barbosa, Luís Miguel Brazão Soares, Maria Inês Dias Ribeiro, and Vítor Simões-Silva. "Gamification in Stroke Rehabilitation." In Advances in Psychology, Mental Health, and Behavioral Studies. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8634-1.ch009.

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Stroke rehabilitation aims to improve patients' abilities to realize daily life activities and, consequently, regain their self-confidence and improve independence and quality of life. Gamification can be defined as the application of game-design elements, dynamics, and principles such as competition, narratives, point-scoring, and awards in non-game contexts, including rehabilitation. It has emerged as a therapeutic alternative or complement to traditional rehabilitation to make motor practice more intense and increase a person's motivation, interest, and satisfaction by bringing meaningful and intrinsically motivational playful experiences. Compared to the same amount of conventional therapy, gamification can increase the number of movements and involve safe and intensive rehabilitation exercises, essential for a successful rehabilitation process.
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Ishii, Akira, Yasuko Kawahata, and Nozomi Okano. "Significant Role of Trust and Distrust in Social Simulation." In The Psychology of Trust [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.101538.

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This paper introduces the Trust-Distrust Model and its applications, extending the Bounded Confidence Model, a theory of opinion dynamics, to include the relationship between trust and mistrust. In recent years, there has been an increase in the number of cases in which the prerequisites for conventional communication (e.g., the other person’s gender, appearance, tone of voice, etc.) cannot be established without the exchange of personal information. However, in recent years, there has been an increase in the use of personal information, such as letters and pictograms “as cryptographic asset data” for two-way communication. However, there are advantages and disadvantages to using information assets in the form of personalized data, which are excerpts of personal information as described above. In the future, the discussion of trust value in the above data will accelerate in indicators such as personal credit scoring. In this paper, the Trust-Distrust Model will be discussed with respect to theories that also address charismatic people, the effects of advertising, and social divisions. Furthermore, simulations of the Trust-Distrust Model show that 55% agreement is sufficient to build social consensus. By addressing this theory, we hope to use it to discuss and predict social risk in future credit scoring discussions.
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Elder, Andrew, Nicholas A. Boon, and Alan G. Japp. "Approach to cardiovascular assessment." In ESC CardioMed. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198784906.003.0001.

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Good clinical skills (history taking, physical examination, diagnostic reasoning, and the ability to explain complex medical problems) are pivotal to high-quality cardiovascular care because they instil confidence in the patient, aid accurate diagnosis, avoid wasteful and pointless investigation, and inform decision-making. The concept of ‘risk’ can be confusing because the term is applied in several different ways. For example, it may be used to describe the likelihood or probability of a certain disease being present (‘diagnostic risk’) or the likelihood of serious harm, particularly death, resulting from the patient’s illness (‘prognostic risk’); it may also be used to denote the likelihood of a healthy individual developing disease at some future date (‘future risk’). The diagnostic utility of individual symptoms and signs, and thus the diagnostic risk, is best expressed as a likelihood ratio and specific scoring systems, created by combining individual features, are also useful to quantify diagnostic risk. The objective of and optimum approach to clinical cardiovascular assessment depends heavily on the context of the consultation. Indeed, adapting the clinician’s chosen approach to the given circumstances is an important skill in itself. Assessing cardiovascular disease in elderly patients with multiple co-morbidities is an increasingly common task that can be particularly challenging.
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Grossman, Avraham. "A Biographical Sketch." In Rashi. Liverpool University Press, 2012. http://dx.doi.org/10.3828/liverpool/9781904113898.003.0002.

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This chapter offers a biographical sketch of Rashi. There are numerous folk legends about Rashi's birth, especially the miracles wrought for his mother during her pregnancy, and about his father and his father's journeys outside France and meetings with various sages, including Maimonides. None of these legends is reliably documented, however, and nothing can be gleaned from them about the events of Rashi's life. Ultimately, they reflect the cultural world of Jewish society in the late Middle Ages—a time that saw the composition, in Jewish circles as in Christian, of numerous hagiographical works recounting the miracles performed for holy men. Rashi is renowned throughout the Jewish world not only for his wide-ranging literary productivity but also for his unique character. Five qualities stand out in his warm and radiant personality: humility and natural simplicity, pursuit of truth, concern for human dignity, great confidence in his own abilities, and a sense of mission as a community leader. These qualities are evident in his actions, his relations with other people, his ties to his students, his world-view, his scorn for arrogance, his love of peace, his literary output, and even in his writing style. The chapter then considers Rashi's status and fame.
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"VII. [The Joy of It That Bold Confidence Is Able in Suffering to Take Power from the World and Has the Power to Change Scorn into Honor, Downfall into Victory]." In Kierkegaard's Writings, XV, edited by Edna H. Hong. Princeton University Press, 1993. http://dx.doi.org/10.1515/9781400832330-015.

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Conference papers on the topic "Confidence scoring"

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Guillevic, Didier, Simona Gandrabur, and Yves Normandin. "Robust semantic confidence scoring." In 7th International Conference on Spoken Language Processing (ICSLP 2002). ISCA, 2002. http://dx.doi.org/10.21437/icslp.2002-291.

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Yoon, Su-Youn, Mark Hasegawa-Johnson, and Richard Sproat. "Automated pronunciation scoring using confidence scoring and landmark-based SVM." In Interspeech 2009. ISCA, 2009. http://dx.doi.org/10.21437/interspeech.2009-551.

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Moreno, Pedro J., Beth Logan, and Bhiksha Raj. "A boosting approach for confidence scoring." In 7th European Conference on Speech Communication and Technology (Eurospeech 2001). ISCA, 2001. http://dx.doi.org/10.21437/eurospeech.2001-497.

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Pao, Christine, Philipp Schmid, and James R. Glass. "Confidence scoring for speech understanding systems." In 5th International Conference on Spoken Language Processing (ICSLP 1998). ISCA, 1998. http://dx.doi.org/10.21437/icslp.1998-430.

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Funayama, Hiroaki, Shota Sasaki, Yuichiroh Matsubayashi, et al. "Preventing Critical Scoring Errors in Short Answer Scoring with Confidence Estimation." In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop. Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.acl-srw.32.

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Duchateau, Demuynck, and Wambacq. "Confidence scoring based on backward language models." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005716.

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Duchateau, Jacques, Kris Demuynck, and Patrick Wambacq. "Confidence scoring based on backward language models." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5743694.

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Punnoose, A. K. "Substate Detection Based Confidence Scoring in Speech Recognition." In 2020 National Conference on Communications (NCC). IEEE, 2020. http://dx.doi.org/10.1109/ncc48643.2020.9056075.

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Duchateau, Jacques, and Patrick Wambacq. "Unconstrained versus constrained acoustic normalisation in confidence scoring." In 7th International Conference on Spoken Language Processing (ICSLP 2002). ISCA, 2002. http://dx.doi.org/10.21437/icslp.2002-481.

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Vanhoucke, Vincent. "Confidence scoring and rejection using multi-pass speech recognition." In Interspeech 2005. ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-266.

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