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Relevant bibliographies by topics / Phoneme segmentation

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Academic literature on the topic 'Phoneme segmentation'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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Journal articles on the topic "Phoneme segmentation"

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Munro, John. "Phoneme awareness span: A neglected dimension of phonemic awareness." Australian Educational and Developmental Psychologist 17, no. 1 (2000): 76–89. http://dx.doi.org/10.1017/s0816512200028042.

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AbstractThe importance of phonemic awareness knowledge in learning to be literate his wellestablished. One dimension of its acquisition, the developmental trend from an implicit awareness of rimes to an explicit awareness of phonemes, has attracted substantial interest.A second dimension, a trend in the amount of phonemic knowledge that can be manipulated, or phonemic awareness span, is examined in the present study. One hundred and sixty children from Preparatory (Prep) to Grade 3 completed five phonological tasks: rhyming, onset-rime segmentation, initial sound recognition, phoneme segmentation, and phoneme substitution. Each task involved words ranging in length from three to five phonemes. Phoneme segmentation and substitution tasks involved words with six phonemes. Over this grade range, phonemic length influenced performance for each task. The nature of the influence varied with grade level; performance for the developmentally simpler tasks was affected at the lower grade levels, whereas the more complex tasks were affected at the higher grades. These trends supported gradual differentiation of phonological knowledge into a network of phonemic units. There are implications for dyslexia subtyping, for reading disabilities diagnosis, and for instructional design.

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LEHTONEN, ANNUKKA, and REBECCA TREIMAN. "Adults' knowledge of phoneme–letter relationships is phonology based and flexible." Applied Psycholinguistics 28, no. 1 (January 2007): 95–114. http://dx.doi.org/10.1017/s0142716406070056.

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Despite the importance of phonemic awareness in beginning literacy, several studies have demonstrated that adults, including teacher trainees, have surprisingly poor phonemic skills. Three experiments investigated whether adults' responses in phonemic awareness and spelling segmentation tasks are based on units larger than single letters and phonemes. Responses often involved large units, and they were influenced by sonority and syllable structure. Participants who performed a phoneme counting task before a spelling segmentation task produced significantly more phoneme-based responses and fewer onset–rime responses than participants who first counted words in sentences. This training effect highlights the flexibility of adults' strategies. Although adults are capable of phoneme-based processing, they sometimes fail to use it.

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Rokhman, Miftakh Farid, Alies Poetri Lintangsari, and Widya Caterine Perdhani. "EFL learners’ phonemic awareness: A correlation between English phoneme identification skill toward word processing." JEES (Journal of English Educators Society) 5, no. 2 (September 12, 2020): 135–41. http://dx.doi.org/10.21070/jees.v5i2.467.

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This research aims to find out the correlation between English phoneme identification skills and word processing. It applies the quantitative approach with correlation design. The participants are 100 of 3rd- semester students in English Language Education Program. The correlational result reveals that it has correlation with .382 degrees in phoneme identification skill toward blending skill with the significance level .000, and .359 degrees in phoneme identification skill toward segmentation skill with the significance level .000. Then, the correlation result of English phoneme identification skill toward word processing is .462 degree with its significance .000. By the result, awareness to identify phoneme by initial, medial, and final sound correlates to the blending and segmenting skills which influence the comprehension of word. The more the students are able to identify phoneme based on its sound, the more the students will be able to blend and segment phoneme. Lastly, the ability to identify English phonemes is proven to be a skill that supports EFL learners on their productive and receptive skills. Then being able to identify its phonemes will assist on recognizing and processing English words appropriately so that English language teaching can be associated with the use of phoneme-based instruction on its teaching process. Highlights : Ability to identify English phonemes is proven to be a skill that supports EFL learners on their English productive and receptive skills. English phoneme identification skill contributes to blending and segmentation skill since phonemic awareness provides both decoding and encoding skill.

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VAN BON, W. H. J., and J. F. J. VAN LEEUWE. "Assessing phonemic awareness in kindergarten: The case for the phoneme recognition task." Applied Psycholinguistics 24, no. 2 (June 2003): 195–219. http://dx.doi.org/10.1017/s0142716403000109.

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The validity of phoneme recognition as an indicator of phonemic awareness at kindergarten age is investigated. Six paper and pencil phonemic awareness (PA) tests, phoneme recognition among them, are administered groupwise to Dutch children a few months before the beginning of formal literacy education. Additional phonological tests and an early reading test are administered individually. Ten months later, children are tested again with PA and literacy tests. Structural equation modeling shows the relations among tests to correspond broadly with findings reported in the literature. The PA test scores are determined by one common factor, and the early PA factor influences later literacy through its influence on later PA skill. Phoneme segmentation has the highest loading on the PA factor, but phoneme recognition is its best paper and pencil representative. Unlike phoneme segmentation, phoneme recognition competence can develop in the absence of literacy skills. Phoneme recognition equals phoneme segmentation in sensitivity and specificity when predicting later literacy failure.

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AINSWORTH, STEPHANIE, STEPHEN WELBOURNE, and ANNE HESKETH. "Lexical restructuring in preliterate children: Evidence from novel measures of phonological representation." Applied Psycholinguistics 37, no. 4 (September 1, 2015): 997–1023. http://dx.doi.org/10.1017/s0142716415000338.

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ABSTRACTThere is substantial debate in the literature surrounding the development of children's phonological representations (PRs). Although infant studies have shown children's representations to contain fine phonetic detail, a consensus is yet to be reached about how and when phonemic categories emerge. This study used novel implicit PR measures with preschool children (n= 38, aged 3 years, 6 months to 4 years, 6 months) to test predictions made by these competing accounts of PR development. The measures were designed to probe PR segmentation at the phoneme (rather than the phone) level without requiring an explicit awareness of phonemes. The results provide evidence in support of vocabulary driven restructuring, with PR segmentation found to be related to vocabulary when controlling for age.

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Carroll, Julia M. "Letter knowledge precipitates phoneme segmentation, but not phoneme invariance." Journal of Research in Reading 27, no. 3 (August 2004): 212–25. http://dx.doi.org/10.1111/j.1467-9817.2004.00228.x.

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Russak, Susie, and Elinor Saiegh-Haddad. "Phonological awareness errors mirror underlying phonological representations: Evidence from Hebrew L1 – English L2 adults." Second Language Research 33, no. 4 (May 3, 2017): 459–82. http://dx.doi.org/10.1177/0267658317703682.

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This article examines the effect of phonological context (singleton vs. clustered consonants) on full phoneme segmentation in Hebrew first language (L1) and in English second language (L2) among typically reading adults (TR) and adults with reading disability (RD) ( n = 30 per group), using quantitative analysis and a fine-grained analysis of errors. In line with earlier findings, overall mean scores revealed significant differences between the two groups. However, no qualitative differences were found. In both groups and languages, full phoneme segmentation overall scores for CVC stimuli were higher than CCVC stimuli. This finding does not align with previous findings, obtained from a phoneme isolation task, showing that isolation from a cohesive CV unit is the most difficult. A fine-grained analysis of errors was conducted to glean insight into this finding. The analysis revealed a preference for creating and preserving CV units in phoneme segmentation in both L1 and L2. This is argued to support the cohesion of the CV unit. The article argues that the effect of language-specific sub-syllabic representations on phonemic analysis may not be always observed in overall scores, yet it is reflected in specific patterns of phonological segmentation errors.

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Zhang, Jin Xi, Hong Zhi Yu, Ning Ma, and Zhao Yao Li. "The Phoneme Automatic Segmentation Algorithms Study of Tibetan Lhasa Words Continuous Speech Stream." Advanced Materials Research 765-767 (September 2013): 2051–54. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2051.

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In this paper, we adopt two methods to voice phoneme segmentation when building Tibetan corpus: One is the traditional artificial segmentation method, one is the automatic segmentation method based on the Mono prime HMM model. And experiments are performed to analyze the accuracy of both methods of segmentations. The results showed: Automatic segmentation method based tone prime HMM model helps to shorten the cycle of building Tibetan corpus, especially in building a large corpus segmentation and labeling a lot of time and manpower cost savings, and have greatly improved the accuracy and consistency of speech corpus annotation information.

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Bouwmeester, Samantha, Elisabeth H. M. van Rijen, and Klaas Sijtsma. "Understanding Phoneme Segmentation Performance by Analyzing Abilities and Word Properties." European Journal of Psychological Assessment 27, no. 2 (January 2011): 95–102. http://dx.doi.org/10.1027/1015-5759/a000049.

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Several studies have demonstrated the relationship between phoneme segmentation ability and early reading performance, but so far it is unclear which abilities are involved, and which word properties contribute to the difficulty level of a segmentation task. Using a sample of 596 Dutch children, we investigated the abilities involved in segmenting the phonemes of 45 pseudowords that differed with respect to several properties. First, we found that a combination of short-term memory and speech perception explained variation in segmentation performance. Second, we found that a limited number of word property effects explained the difficulty level of pseudowords rather well. Finally, we constructed a high-reliability scale for measuring segmentation ability.

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Murray, Bruce A., Edna G. Brabham, Susan K. Villaume, and Margo Veal. "The Cluella Study: Optimal Segmentation and Voicing for Oral Blending." Journal of Literacy Research 40, no. 4 (October 1, 2008): 395–421. http://dx.doi.org/10.1080/10862960802629197.

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Using a within-subjects design, researchers compared 183 kindergartners' ability to blend words segmented in four different ways. On digitized video, a researcher portraying the fictitious “Cluella” pronounced 40 consonant-vowel-consonant words for participants to blend. She gave 10 words in phoneme segments with a loud schwa added to consonants, 10 in phoneme segments minimizing any schwa voicing, 10 in onset-rime chunks, and 10 in body-coda chunks. Participants were significantly more successful blending body-coda chunks than blending onsetrime chunks. They also were significantly more successful blending phonemes with added schwa than without. We apply these results to suggest procedures for providing optimal scaffolds for helping children learn to identify words.

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Dissertations / Theses on the topic "Phoneme segmentation"

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Durst, Elizabeth Ann. "Scaffolding Preschoolers' Acquisition, Maintenance, and Generalization of Phoneme Segmentation Skills Using Sound Boxes." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1368707491.

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Hsin, Yi-Wei. "Effects of phonological awareness instruction on pre-reading skills of preschool children at-risk for reading disabilities." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187295981.

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Andrla, Petr. "Segmentace řeči." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218262.

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The programme for the segmentation of a speech into fonems was created as a part of the master´s thesis. This programme was made in the programme Matlab and consists of several scripts. The programme serves for automatic segmentation. Speech segmentation is the process of identifying the boundaries between phonemes in spoken natural languages. Automatic segmentation is based on vector quantization. In the first step of algorithm, feature extraction is realized. Then speech segments are assigned to calculated centroids. Position where centroid is changed is marked as a boundary of phoneme. The audiorecords were elaborated by the programme and a operation of the automatic segmentation was analysed. A detailed manual was created to the programme too. Individual used methods of the elaboration of a speech were in the master´s thesis briefly descripted, its implementations in the programme and reasons of set of its parameters.

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Raybaud, Sylvain. "De l'utilisation de mesures de confiance en traduction automatique : évaluation, post-édition et application à la traduction de la parole." Thesis, Université de Lorraine, 2012. http://www.theses.fr/2012LORR0260/document.

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Cette thèse de doctorat aborde les problématiques de l'estimation de confiance pour la traduction automatique, et de la traduction automatique statistique de la parole spontanée à grand vocabulaire. J'y propose une formalisation du problème d'estimation de confiance, et aborde expérimentalement le problème sous le paradigme de la classification et régression multivariée. Je propose une évaluation des performances des différentes méthodes évoquées, présente les résultats obtenus lors d'une campagne d'évaluation internationale et propose une application à la post-édition par des experts de documents traduits automatiquement. J'aborde ensuite le problème de la traduction automatique de la parole. Après avoir passé en revue les spécificités du medium oral et les défis particuliers qu'il soulève, je propose des méthodes originales pour y répondre, utilisant notamment les réseaux de confusion phonétiques, les mesures de confiances et des techniques de segmentation de la parole. Je montre finalement que le prototype propose rivalise avec des systèmes état de l'art à la conception plus classique
In this thesis I shall deal with the issues of confidence estimation for machine translation and statistical machine translation of large vocabulary spontaneous speech translation. I shall first formalize the problem of confidence estimation. I present experiments under the paradigm of multivariate classification and regression. I review the performances yielded by different techniques, present the results obtained during the WMT2012 internation evaluation campaign and give the details of an application to post edition of automatically translated documents. I then deal with the issue of speech translation. After going into the details of what makes it a very specific and particularly challenging problem, I present original methods to partially solve it, by using phonetic confusion networks, confidence estimation techniques and speech segmentation. I show that the prototype I developped yields performances comparable to state-of-the-art of more standard design

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Magnan, Joselyn Emily. "The Efficacy of Training Kindergartners in Assisted Self-Graphing as a Supplemental Intervention Within a Response-To-Intervention Model." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1154960495.

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Cubelic, Cathleen J. "iPad 2 Applications and Emergent Literacy: Do They Have an Impact on the Acquisition of Early Literacy Skills?" Youngstown State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1370348007.

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Sun, Felice (Felice Tzu-yun) 1976. "Integrating statistical and knowledge-based methods for automatic phonemic segmentation." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80127.

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Kašpar, Ladislav. "Segmentace řeči." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-220414.

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My diploma thesis is devoted to the problem of segmentation of speech. It includes the basic theory on this topic. The theory focuses on the calculation of parameters for seg- mentation of speech that are used in the practical part. An application for segmentation of speech has been written in Matlab. It uses techniques as segmentation of the signal, energy of the signal and zero crossing function. These parameters are used as input for the algorithm k–means.

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Nefti, Samir. "Segmentation automatique de parole en phones : Correction d'étiquetage par l'introduction de mesures de confiance." Phd thesis, Université Rennes 1, 2004. http://tel.archives-ouvertes.fr/tel-00122091.

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Un système de synthèse de parole par concaténation d'unités acoustiques utilise un dictionnaire de ces unités, construit à partir d'un corpus de parole mono-locuteur segmentée en éléments acoustiques, généralement phonétiques. Pour atteindre une qualité de parole synthétique suffisante, ce dictionnaire doit être richement fourni, et par conséquent nécessite un corpus de plusieurs heures de parole.
La segmentation manuelle d'un tel corpus de parole est fastidieuse, d'où l'intérêt de la segmentation automatique. À condition de disposer des transcriptions phonétiques réelles des énoncés, les méthodes automatiques produisent une segmentation de qualité approximativement équivalente à celle d'une segmentation manuelle. Cependant, la transcription manuelle du contenu phonétique du corpus de parole est également fastidieuse.
Cette étude concerne la segmentation automatique de parole en phones qui utilise des transcriptions phonétiques automatiquement produites à partir du texte. Elle porte sur la détection et la correction des erreurs d'étiquetage phonétique que contiennent généralement ces transcriptions phonétiques automatiques. Les résultats obtenus dans cette étude sont significativement positifs.

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Pate, John K. "Extending phone prediction models of word segmentation to a more realistic representation of prosody." Connect to resource, 2009. http://hdl.handle.net/1811/37257.

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Books on the topic "Phoneme segmentation"

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Sarma, Mousmita, and Kandarpa Kumar Sarma. Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3.

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Allen, Tracy L. Reciprocal view of phonemic awareness and beginning word recognition among prereaders: Segmentation versus whole word. Sudbury, Ont: Laurentian University, Department of Psychology, 1998.

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Sarma, Kandarpa Kumar, and Mousmita Sarma. Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework. Springer, 2014.

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Sarma, Kandarpa Kumar, and Mousmita Sarma. Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework. Springer, 2016.

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Webber Phonological Awareness Photo Cards Segmentation of Phonemes, WPA-09 (Webber Phonological Awareness). Super Duper, 2005.

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Book chapters on the topic "Phoneme segmentation"

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "Phoneme Segmentation Technique Using Self-Organizing Map (SOM)." In Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 117–35. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3_6.

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "Application of Phoneme Segmentation Technique in Spoken Word Recognition." In Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 137–52. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3_7.

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "Application of Proposed Phoneme Segmentation Technique for Speaker Identification." In Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 163–82. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3_9.

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "Introduction." In Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 3–19. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3_1.

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "Conclusion." In Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 183–84. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3_10.

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "Speech Processing Technology: Basic Consideration." In Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 21–45. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3_2.

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "Fundamental Considerations of ANN." In Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 47–75. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3_3.

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "Sounds of Assamese Language." In Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 77–93. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3_4.

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "State of Research of Speech Recognition." In Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 95–113. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3_5.

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Sarma, Mousmita, and Kandarpa Kumar Sarma. "Application of Clustering Techniques to Generate a Priori Knowledge for Spoken Word Recognition." In Phoneme-Based Speech Segmentation using Hybrid Soft Computing Framework, 153–62. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1862-3_8.

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Conference papers on the topic "Phoneme segmentation"

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Zioko, B., S. Manandhar, and R. C. Wilson. "Phoneme segmentation of speech." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.931.

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Goh, Y. H., and P. Raveendran. "Phoneme segmentation of speech signal." In 2009 International Conference for Technical Postgraduates (TECHPOS). IEEE, 2009. http://dx.doi.org/10.1109/techpos.2009.5412045.

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Geetha, K., and E. Chandra. "Automatic phoneme segmentation of Tamil utterances." In 2015 International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2015. http://dx.doi.org/10.1109/icaccs.2015.7324062.

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Qiao, Yu, and Nobuaki Minematsu. "Metric learning for unsupervised phoneme segmentation." In Interspeech 2008. ISCA: ISCA, 2008. http://dx.doi.org/10.21437/interspeech.2008-328.

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Gong, Rong, and Xavier Serra. "Singing Voice Phoneme Segmentation by Hierarchically Inferring Syllable and Phoneme Onset Positions." In Interspeech 2018. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/interspeech.2018-1224.

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Vetter, Marco, Markus Müller, Fatima Hamlaoui, Graham Neubig, Satoshi Nakamura, Sebastian Stüker, and Alex Waibel. "Unsupervised Phoneme Segmentation of Previously Unseen Languages." In Interspeech 2016. ISCA, 2016. http://dx.doi.org/10.21437/interspeech.2016-1440.

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Michel, Paul, Okko Räsänen, Roland Thiollière, and Emmanuel Dupoux. "Blind Phoneme Segmentation With Temporal Prediction Errors." In Proceedings of ACL 2017, Student Research Workshop. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/p17-3011.

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Kalinli, Ozlem. "Automatic phoneme segmentation using auditory attention features." In Interspeech 2012. ISCA: ISCA, 2012. http://dx.doi.org/10.21437/interspeech.2012-596.

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Palaz, Dimitri, Mathew Magimai-Doss, and Ronan Collobert. "Joint phoneme segmentation inference and classification using CRFs." In 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2014. http://dx.doi.org/10.1109/globalsip.2014.7032185.

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Kreuk, Felix, Joseph Keshet, and Yossi Adi. "Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation." In Interspeech 2020. ISCA: ISCA, 2020. http://dx.doi.org/10.21437/interspeech.2020-2398.

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Reports on the topic "Phoneme segmentation"

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Andrews, David. A Comparative Study of Phonemic Segmentation Skills in First Grade Children with Normal, Disordered, and Slow Expressive Language Development. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6634.

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