Journal articles: 'Word recognition models'

1

Norris, Dennis. "Models of visual word recognition." Trends in Cognitive Sciences 17, no. 10 (October 2013): 517–24. http://dx.doi.org/10.1016/j.tics.2013.08.003.

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2

Weber, Andrea, and Odette Scharenborg. "Models of spoken-word recognition." Wiley Interdisciplinary Reviews: Cognitive Science 3, no. 3 (April 2, 2012): 387–401. http://dx.doi.org/10.1002/wcs.1178.

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3

Seidenberg, Mark S. "Constraining models of word recognition." Cognition 20, no. 2 (July 1985): 169–90. http://dx.doi.org/10.1016/0010-0277(85)90052-6.

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4

Koerich, A. L. "Handwritten Word Recognition Using Markov Models." IEEE Latin America Transactions 2, no. 2 (June 2004): 132–41. http://dx.doi.org/10.1109/tla.2004.1468632.

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Koerich, A. L. "Handwritten Word Recognition Using Markov Models." IEEE Latin America Transactions 2, no. 2 (June 2004): 58–67. http://dx.doi.org/10.1109/tla.2004.1642391.

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Amano, Shigeaki. "Overview of models on spoken word recognition." Japanese journal of psychology 70, no. 3 (1999): 228–40. http://dx.doi.org/10.4992/jjpsy.70.228.

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Seidenberg, Mark S. "Explanatory adequacy and models of word recognition." Behavioral and Brain Sciences 8, no. 4 (December 1985): 724–26. http://dx.doi.org/10.1017/s0140525x00045921.

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Seidenberg, Mark S., David C. Plaut, Alan S. Petersen, James L. McClelland, and Ken McRae. "Nonword pronunciation and models of word recognition." Journal of Experimental Psychology: Human Perception and Performance 20, no. 6 (1994): 1177–96. http://dx.doi.org/10.1037/0096-1523.20.6.1177.

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9

Jianing Dai. "Isolated word recognition using Markov chain models." IEEE Transactions on Speech and Audio Processing 3, no. 6 (1995): 458–63. http://dx.doi.org/10.1109/89.482213.

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Seidenberg, Mark S., Alan Petersen, Maryellen C. MacDonald, and David C. Plaut. "Pseudohomophone effects and models of word recognition." Journal of Experimental Psychology: Learning, Memory, and Cognition 22, no. 1 (1996): 48–62. http://dx.doi.org/10.1037/0278-7393.22.1.48.

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Liversedge, Simon P., Hazel I. Blythe, and Denis Drieghe. "Beyond isolated word recognition." Behavioral and Brain Sciences 35, no. 5 (August 29, 2012): 293–94. http://dx.doi.org/10.1017/s0140525x12000210.

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AbstractIn this commentary we concur with Frost's view of the centrality of universal principles in models of word identification. However, we argue that other processes in sentence comprehension also fundamentally constrain the nature of written word identification. Furthermore, these processes appear to be universal. We, therefore, argue that universality in word identification should not be considered in isolation, but instead in the context of other linguistic processes that occur during normal reading.

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12

Suomi, Kari. "On Detecting Words and Word Boundaries in Finnish: A Survey of Potential Word Boundary Signals." Nordic Journal of Linguistics 8, no. 2 (December 1985): 211–31. http://dx.doi.org/10.1017/s0332586500001347.

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Two models are presented of how the listener detects words in utterances. The first model assumes that the listener takes advantage of phonetic word boundary signals (WBSs), non-phonetic information not being necessary for word detection. The second model assumes that word detection relies on the use of non-phonetic knowledge of the language, words being detected through the recognition of the preceding word. Thus WBSs may not be necessary for word detection. The WBSs suggested for Finnish are evaluated against this background. The phonotactic WBSs are found unreliable or useless, the others limited in applicability or experimentally unsupported. The models and the results of the survey will direct future investigations of word detection and recognition in Finnish.

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Voormann, Anne, Mikhail S. Spektor, and Karl Christoph Klauer. "The simultaneous recognition of multiple words: A process analysis." Memory & Cognition 49, no. 4 (April 8, 2021): 787–802. http://dx.doi.org/10.3758/s13421-020-01082-w.

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AbstractIn everyday life, recognition decisions often have to be made for multiple objects simultaneously. In contrast, research on recognition memory has predominantly relied on single-item recognition paradigms. We present a first systematic investigation into the cognitive processes that differ between single-word and paired-word tests of recognition memory. In a single-word test, participants categorize previously presented words and new words as having been studied before (old) or not (new). In a paired-word test, however, the test words are randomly paired, and participants provide joint old–new categorizations of both words for each pair. Across two experiments (N = 170), we found better memory performance for words tested singly rather than in pairs and, more importantly, dependencies between the two single-word decisions implied by the paired-word test. We extended two popular model classes of single-item recognition to paired-word recognition, a discrete-state model and a continuous model. Both models attribute performance differences between single-word and paired-word recognition to differences in memory-evidence strength. Discrete-state models account for the dependencies in paired-word decisions in terms of dependencies in guessing. In contrast, continuous models map the dependencies on mnemonic (Experiment 1 & 2) as well as on decisional processes (Experiment 2). However, in both experiments, model comparison favored the discrete-state model, indicating that memory decisions for word pairs seem to be mediated by discrete states. Our work suggests that individuals tackle multiple-item recognition fundamentally differently from single-item recognition, and it provides both a behavioral and model-based paradigm for studying multiple-item recognition.

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BRYSBAERT, MARC, NELE VERREYT, and WOUTER DUYCK. "Models as hypothesis generators and models as roadmaps." Bilingualism: Language and Cognition 13, no. 3 (March 22, 2010): 383–84. http://dx.doi.org/10.1017/s1366728910000167.

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In this reply to Kroll, Van Hell, Tokowicz and Green (this issue) we present an analysis of the citations made to the Revised Hierarchical Model (RHM). This gives us a quantitative summary of the current use of the RHM, showing that RHM has been used equally often to guide research in word recognition as in word production. We also question the claim that Brysbaert and Duyck's (this issue) focus on word recognition leaves RHM unscathed for the explanation of word production and the interactions between lexical and conceptual representations. For these research topics too, we feel that more progress will be made by adapting computational monolingual models to the bilingual situation rather than by trying to understand the findings from the RHM framework.

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15

Chou, Wu. "Recognition unit model training based on competing word and word string models." Journal of the Acoustical Society of America 102, no. 3 (September 1997): 1284. http://dx.doi.org/10.1121/1.420029.

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16

Thalengala, Ananthakrishna, and Kumara Shama. "Study of sub-word acoustical models for Kannada isolated word recognition system." International Journal of Speech Technology 19, no. 4 (October 1, 2016): 817–26. http://dx.doi.org/10.1007/s10772-016-9374-0.

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17

Scharenborg, Odette, and Lou Boves. "Computational modelling of spoken-word recognition processes." Pragmatics and Cognition 18, no. 1 (April 9, 2010): 136–64. http://dx.doi.org/10.1075/pc.18.1.06sch.

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Computational modelling has proven to be a valuable approach in developing theories of spoken-word processing. In this paper, we focus on a particular class of theories in which it is assumed that the spoken-word recognition process consists of two consecutive stages, with an ‘abstract’ discrete symbolic representation at the interface between the stages. In evaluating computational models, it is important to bring in independent arguments for the cognitive plausibility of the algorithms that are selected to compute the processes in a theory. This paper discusses the relation between behavioural studies, theories, and computational models of spoken-word recognition. We explain how computational models can be assessed in terms of the goodness of fit with the behavioural data and the cognitive plausibility of the algorithms. An in-depth analysis of several models provides insights into how computational modelling has led to improved theories and to a better understanding of the human spoken-word recognition process.

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18

Ming, J., and F. J. Smith. "Isolated word recognition using interframe dependent hidden Markov models." IEEE Signal Processing Letters 1, no. 12 (December 1994): 188–90. http://dx.doi.org/10.1109/97.338748.

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19

Odell, Julian J., and Shahid Durrani. "Word-specific acoustic models in a speech recognition system." Journal of the Acoustical Society of America 120, no. 6 (2006): 3454. http://dx.doi.org/10.1121/1.2409463.

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Meylan, Stephan C., Sathvik Nair, and Thomas L. Griffiths. "Evaluating models of robust word recognition with serial reproduction." Cognition 210 (May 2021): 104553. http://dx.doi.org/10.1016/j.cognition.2020.104553.

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21

Jemni, Sana Khamekhem, Yousri Kessentini, and Slim Kanoun. "Improving Recurrent Neural Networks for Offline Arabic Handwriting Recognition by Combining Different Language Models." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 12 (April 21, 2020): 2052007. http://dx.doi.org/10.1142/s0218001420520072.

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In handwriting recognition, the design of relevant features is very important, but it is a daunting task. Deep neural networks are able to extract pertinent features automatically from the input image. This drops the dependency on handcrafted features, which is typically a trial and error process. In this paper, we perform an exhaustive experimental evaluation of learned against handcrafted features for Arabic handwriting recognition task. Moreover, we focus on the optimization of the competing full-word based language models by incorporating different characters and sub-words models. We extensively investigate the use of different sub-word-based language models, mainly characters, pseudo-words, morphemes and hybrid units in order to enhance the full-word handwriting recognition system for Arabic script. The proposed method allows the recognition of any out of vocabulary word as an arbitrary sequence of sub-word units. The KHATT database has been used as a benchmark for the Arabic handwriting recognition. We show that combining multiple language models enhances considerably the recognition performance for a morphologically rich language like Arabic. We achieve the state-of-the-art performance on the KHATT dataset.

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22

Zhang, Y. "Speaker-independent isolated word recognition using multiple hidden Markov models." IEE Proceedings - Vision, Image, and Signal Processing 141, no. 3 (1994): 197. http://dx.doi.org/10.1049/ip-vis:19941142.

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23

Gillick, Laurence. "Method for representing word models for use in speech recognition." Journal of the Acoustical Society of America 92, no. 1 (July 1992): 629. http://dx.doi.org/10.1121/1.404106.

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Mohamed, M. A., and P. Gader. "Generalized hidden Markov models. II. Application to handwritten word recognition." IEEE Transactions on Fuzzy Systems 8, no. 1 (2000): 82–94. http://dx.doi.org/10.1109/91.824774.

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25

Bahl, L. R., J. R. Bellegarda, P. V. de Souza, P. S. Gopalakrishnan, D. Nahamoo, and M. A. Picheny. "Multonic Markov word models for large vocabulary continuous speech recognition." IEEE Transactions on Speech and Audio Processing 1, no. 3 (July 1993): 334–44. http://dx.doi.org/10.1109/89.232617.

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26

Mari, J. F., J. P. Haton, and A. Kriouile. "Automatic word recognition based on second-order hidden Markov models." IEEE Transactions on Speech and Audio Processing 5, no. 1 (1997): 22–25. http://dx.doi.org/10.1109/89.554265.

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27

Hogden, John, David Nix, Vincent Gracco, and Philip Rubin. "Stochastic word models for articulatorily constrained speech recognition and synthesis." Journal of the Acoustical Society of America 103, no. 5 (May 1998): 2774. http://dx.doi.org/10.1121/1.421411.

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28

Pitrelli, John F., and Amit Roy. "Creating word-level language models for large-vocabulary handwriting recognition." International Journal on Document Analysis and Recognition 5, no. 2-3 (April 1, 2003): 126–37. http://dx.doi.org/10.1007/s10032-002-0087-3.

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29

Elzobi, Moftah, and Ayoub Al-Hamadi. "Generative vs. Discriminative Recognition Models for Off-Line Arabic Handwriting." Sensors 18, no. 9 (August 24, 2018): 2786. http://dx.doi.org/10.3390/s18092786.

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The majority of handwritten word recognition strategies are constructed on learning-based generative frameworks from letter or word training samples. Theoretically, constructing recognition models through discriminative learning should be the more effective alternative. The primary goal of this research is to compare the performances of discriminative and generative recognition strategies, which are described by generatively-trained hidden Markov modeling (HMM), discriminatively-trained conditional random fields (CRF) and discriminatively-trained hidden-state CRF (HCRF). With learning samples obtained from two dissimilar databases, we initially trained and applied an HMM classification scheme. To enable HMM classifiers to effectively reject incorrect and out-of-vocabulary segmentation, we enhance the models with adaptive threshold schemes. Aside from proposing such schemes for HMM classifiers, this research introduces CRF and HCRF classifiers in the recognition of offline Arabic handwritten words. Furthermore, the efficiencies of all three strategies are fully assessed using two dissimilar databases. Recognition outcomes for both words and letters are presented, with the pros and cons of each strategy emphasized.

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30

Rueckl, Jay. "Connectionism and the role of morphology in visual word recognition." Mental Lexicon 5, no. 3 (December 31, 2010): 371–400. http://dx.doi.org/10.1075/ml.5.3.07rue.

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This paper provides a review of the connectionist perspective on the role of morphology in visual word recognition. Several computational models of morphological effects in reading are described and relationships between these models, models of past tense production, and models of other aspects of word recognition are traced. Limitations of extant models are noted, as are some of the technical challenges that must be solved to develop the next generation of models. Finally, some directions for future research are identified.

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31

RUIZ-PINALES, JOSE, RENE JAIME-RIVAS, ERIC LECOLINET, and MARIA JOSE CASTRO-BLEDA. "CURSIVE WORD RECOGNITION BASED ON INTERACTIVE ACTIVATION AND EARLY VISUAL PROCESSING MODELS." International Journal of Neural Systems 18, no. 05 (October 2008): 419–31. http://dx.doi.org/10.1142/s0129065708001683.

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We present an off-line cursive word recognition system based completely on neural networks: reading models and models of early visual processing. The first stage (normalization) preprocesses the input image in order to reduce letter position uncertainty; the second stage (feature extraction) is based on the feedforward model of orientation selectivity; the third stage (letter pre-recognition) is based on a convolutional neural network, and the last stage (word recognition) is based on the interactive activation model.

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Hutchinson, T. P. "On a Recognition Task in Which Some Distractors Were Half-Familiar." International Journal of Aging and Human Development 46, no. 1 (January 1998): 21–24. http://dx.doi.org/10.2190/ap1r-ae7m-weeu-km4y.

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Models are suggested for the task of recognizing word-pairs, where the distractors may be pairs of new words, or may be a new word paired with a previously-seen word. These models are relevant to an experiment recently reported by Isingrini et al., and suggest rather different conclusions to theirs—namely, that the elderly differ from the young in both learning and response selection.

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González-Alvarez, Julio, and María-Angeles Palomar-García. "Syllable Frequency and Spoken Word Recognition." Psychological Reports 119, no. 1 (July 22, 2016): 263–75. http://dx.doi.org/10.1177/0033294116654449.

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Research has shown that syllables play a relevant role in lexical access in Spanish, a shallow language with a transparent syllabic structure. Syllable frequency has been shown to have an inhibitory effect on visual word recognition in Spanish. However, no study has examined the syllable frequency effect on spoken word recognition. The present study tested the effect of the frequency of the first syllable on recognition of spoken Spanish words. A sample of 45 young adults (33 women, 12 men; M = 20.4, SD = 2.8; college students) performed an auditory lexical decision on 128 Spanish disyllabic words and 128 disyllabic nonwords. Words were selected so that lexical and first syllable frequency were manipulated in a within-subject 2 × 2 design, and six additional independent variables were controlled: token positional frequency of the second syllable, number of phonemes, position of lexical stress, number of phonological neighbors, number of phonological neighbors that have higher frequencies than the word, and acoustical durations measured in milliseconds. Decision latencies and error rates were submitted to linear mixed models analysis. Results showed a typical facilitatory effect of the lexical frequency and, importantly, an inhibitory effect of the first syllable frequency on reaction times and error rates.

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Ghai, Wiqas, and Navdeep Singh. "Phone based acoustic modeling for automatic speech recognition for Punjabi language." Journal of Speech Sciences 3, no. 1 (February 5, 2021): 68–83. http://dx.doi.org/10.20396/joss.v3i1.15040.

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Punjabi language is a tonal language belonging to an Indo-Aryan language family and has a number of speakers all around the world. Punjabi language has gained acceptability in the media & communication and therefore deserves to have a place in the growing field of automatic speech recognition which has been explored already for a number of other Indian and foreign languages successfully. Some work has been done in the field of isolated word speech recognition for Punjabi language, but only using whole word based acoustic models. A phone based approach has yet to be applied for Punjabi language speech recognition. This paper describes an automatic speech recognizer that recognizes isolated word speech and connected word speech using a triphone based acoustic model on the HTK 3.4.1 speech Engine and compares the performance with acoustic whole word model based ASR system. Word recognition accuracy of isolated word speech was 92.05% for acoustic whole word model based system and 97.14% for acoustic triphone model based system whereas word recognition accuracy of connected word speech was 87.75% for acoustic whole word model based system and 91.62% for acoustic triphone model based system.

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Altieri, Nicholas, Thomas Gruenenfelder, and David B. Pisoni. "Clustering coefficients of lexical neighborhoods." Mental Lexicon 5, no. 1 (June 18, 2010): 1–21. http://dx.doi.org/10.1075/ml.5.1.01alt.

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High neighborhood density reduces the speed and accuracy of spoken word recognition. The two studies reported here investigated whether Clustering Coefficient (CC) — a graph theoretic variable measuring the degree to which a word’s neighbors are neighbors of one another, has similar effects on spoken word recognition. In Experiment 1, we found that high CC words were identified less accurately when spectrally degraded than low CC words. In Experiment 2, using a word repetition procedure, we observed longer response latencies for high CC words compared to low CC words. Taken together, the results of both studies indicate that higher CC leads to slower and less accurate spoken word recognition. The results are discussed in terms of activation-plus-competition models of spoken word recognition.

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DIJKSTRA, TON, ALEXANDER WAHL, FRANKA BUYTENHUIJS, NINO VAN HALEM, ZINA AL-JIBOURI, MARCEL DE KORTE, and STEVEN REKKÉ. "Multilink: a computational model for bilingual word recognition and word translation." Bilingualism: Language and Cognition 22, no. 04 (July 6, 2018): 657–79. http://dx.doi.org/10.1017/s1366728918000287.

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The computational BIA+ model (Dijkstra & Van Heuven, 2002) has provided a useful account for bilingual word recognition, while the verbal (pre-quantitative) RHM (Kroll & Stewart, 1994) has often served as a reference framework for bilingual word production and translation. According to Brysbaert and Duyck (2010), a strong need is felt for a unified implemented account of bilingual word comprehension, lexical-semantic processing, and word production. With this goal in mind, we built a localist-connectionist model, called Multilink, which integrates basic assumptions of both BIA+ and RHM. It simulates the recognition and production of cognates (form-similar translation equivalents) and non-cognates of different lengths and frequencies in tasks like monolingual and bilingual lexical decision, word naming, and word translation production. It also considers effects of lexical similarity, cognate status, relative L2-proficiency, and translation direction. Model-to-model comparisons show that Multilink provides higher correlations with empirical data than both IA and BIA+ models.

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37

Kessentini, Yousri, Thierry Paquet, and AbdelMajid Ben Hamadou. "Off-line handwritten word recognition using multi-stream hidden Markov models." Pattern Recognition Letters 31, no. 1 (January 2010): 60–70. http://dx.doi.org/10.1016/j.patrec.2009.08.009.

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Emillia, Nyoman Rizkha, Suyanto Suyanto, and Warih Maharani. "Isolated Word Recognition Using Ergodic Hidden Markov Models and Genetic Algorithm." TELKOMNIKA (Telecommunication Computing Electronics and Control) 10, no. 1 (March 1, 2012): 129. http://dx.doi.org/10.12928/telkomnika.v10i1.769.

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39

Jacobs, Arthur M., and Jonathan Grainger. "Models of visual word recognition: Sampling the state of the art." Journal of Experimental Psychology: Human Perception and Performance 20, no. 6 (1994): 1311–34. http://dx.doi.org/10.1037/0096-1523.20.6.1311.

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40

Bocchieri, E. L., and J. G. Wilpon. "Speaker‐independent speech recognition with word models generated from written text." Journal of the Acoustical Society of America 89, no. 4B (April 1991): 1937. http://dx.doi.org/10.1121/1.2029583.

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41

Shi, Lu-Feng. "Validating Models of Clinical Word Recognition Tests for Spanish/English Bilinguals." Journal of Speech, Language, and Hearing Research 57, no. 5 (October 2014): 1896–907. http://dx.doi.org/10.1044/2014_jslhr-h-13-0138.

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Barber, Horacio A., and Marta Kutas. "Interplay between computational models and cognitive electrophysiology in visual word recognition." Brain Research Reviews 53, no. 1 (January 2007): 98–123. http://dx.doi.org/10.1016/j.brainresrev.2006.07.002.

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43

Deng, L., M. Lennig, F. Seitz, and P. Mermelstein. "Large vocabulary word recognition using context-dependent allophonic hidden Markov models." Computer Speech & Language 4, no. 4 (October 1990): 345–57. http://dx.doi.org/10.1016/0885-2308(90)90015-x.

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Cortese, Michael J., David Von Nordheim, and Maya M. Khanna. "Word length negatively predicts recognition memory performance." Quarterly Journal of Experimental Psychology 73, no. 10 (May 24, 2020): 1675–83. http://dx.doi.org/10.1177/1747021820921133.

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We examined how word length affects performance in three recognition memory experiments to resolve discrepant results in the literature for which there are theoretical implications. Shorter and longer words were equated on frequency, orthographic similarity, age of acquisition, and imageability. In Experiments 1 and 2, orthographic length (i.e., the number of letters in a word) was negatively related to hits minus false alarms. In Experiment 3, recognition performance did not differ between one- and two-syllable words that were equated on orthographic length. These results are compatible with single-process item-noise models that represent orthography in terms of features and in which memory representation strength is a product of the probabilities that the individual features have been stored. Longer words are associated with noisier representations than shorter words.

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Heyman, Tom, and Geert Heyman. "Can prediction-based distributional semantic models predict typicality?" Quarterly Journal of Experimental Psychology 72, no. 8 (February 21, 2019): 2084–109. http://dx.doi.org/10.1177/1747021819830949.

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Recent advances in the field of computational linguistics have led to the development of various prediction-based models of semantics. These models seek to infer word representations from large text collections by predicting target words from neighbouring words (or vice versa). The resulting representations are vectors in a continuous space, collectively called word embeddings. Although psychological plausibility was not a primary concern for the developers of predictive models, it has been the topic of several recent studies in the field of psycholinguistics. That is, word embeddings have been linked to similarity ratings, word associations, semantic priming, word recognition latencies, and so on. Here, we build on this work by investigating category structure. Throughout seven experiments, we sought to predict human typicality judgements from two languages, Dutch and English, using different semantic spaces. More specifically, we extracted a number of predictor variables, and evaluated how well they could capture the typicality gradient of common categories (e.g., birds, fruit, vehicles, etc.). Overall, the performance of predictive models was rather modest and did not compare favourably with that of an older count-based model. These results are somewhat disappointing given the enthusiasm surrounding predictive models. Possible explanations and future directions are discussed.

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Paciorek, Wiktor, and Joanna Rączaszek-Leonardi. "The influence of sentential context and frequency of occurrence on the recognition of words with scrambled letters." Psychology of Language and Communication 13, no. 2 (January 1, 2009): 45–57. http://dx.doi.org/10.2478/v10057-009-0010-9.

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The influence of sentential context and frequency of occurrence on the recognition of words with scrambled letters In this paper we examine the "jumbled words" effect which denotes human ability to easily read words whose internal letters have been re-arranged as long as external letters remain in their positions. Hitherto, many explanations for this effect have focussed on the processes that operate "bottom-up". Here we suggest that "top-down" processes also play an important role and demonstrate this experimentally. First, we briefly describe the main types of word-recognition models and consider which model best explains the effect. Then, we present an experiment in which jumbled words of different frequency of occurrence were immersed in various types of contexts. Results indicate that both the frequency and semantic sentential context are involved in jumbled word recognition. The implications of these findings for word recognition models are discussed.

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Ringelienė, Živilė, and Mark Filipovič. "Žodžių atpažinimo, grįsto paslėptaisiais Markovo modeliais, vizualizavimo ir analizės programinė įranga." Informacijos mokslai 56 (January 1, 2011): 63–72. http://dx.doi.org/10.15388/im.2011.0.3150.

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Straipsnyje aprašomas atpažinimo, grįsto paslėptaisiais Markovo modeliais, sistemos prototipo veikimas. Ši programinė įranga skirta lietuvių kalbos žodžių atpažinimui tirti. Nagrinėjama, kaip sistemos pateikiama informacija apie žodžių atpažinimo procesą ir rezultatus padeda analizuoti klaidų priežastis. Žodžio atpažinimas priklauso nuo žodžio ribų nustatymo tikslumo. Signalo, energijos, žodžio ribų vizualizavimas leidžia lengviau įvertinti, ar sistema teisingai nustatė ribas. Jei žodis atpažintasklaidingai dėl to, kad buvo blogai nustatytos ribos, galima keisti sistemos parametrų, darančių įtaką ribų nustatymo tikslumui, reikšmes. Tam tikrais atvejais tai pagerina atpažinimo rezultatus. Žodžio paieškos vaizdavimas padeda įvertinti kiekvieno fonemos modelio įtaką žodžio atpažinimui ir parinkti žodžių transkripcijas, kurios pagerina atpažinimo rezultatus.A Tool for Visualization and Analysis of Isolated Word Recognition Based on the Hidden Markov ModelsŽivilė Ringelienė, Mark Filipovič SummaryThe paper presents a prototype of the isolated word recognition system based on hidden Markov models. The developed prototype of the speakerindependent Lithuanian isolated word recognition system is handy for recognition experiments and the analysis of their results. The user is provided with numeric and visual recognition information on the results. The word recognition pivots on the precision of the determination of the word limits. The main window contains a recognized word and its logarithmic likelihood, a visible waveform of the speech signal, the depicted energy of the speech signal, the identified word boundaries and energy detection thresholds. If the system misrecognized the word, such visualization enables to identify easier whether it resulted from wrong end-point detection. The segmentation window provides with a list of words which acoustic models to the given speech signal are the best, the scores of their likelihood and a diagram of the most likely sequence of the phoneme models aligned with the speech signal. Such visualization helps to analyze recognition errors and the impact of each phoneme model on the recognition accuracy. Results of preliminary experiments have shown that by changing the transcription of some words the recognition accuracy can be increased.

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Roldán, Manuel, Ana Marcet, and Manuel Perea. "Is there a cost at encoding words with joined letters during visual word recognition?" Psicológica Journal 39, no. 2 (July 1, 2018): 279–91. http://dx.doi.org/10.2478/psicolj-2018-0012.

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AbstractFor simplicity, models of visual-word recognition have focused on printed words composed of separated letters, thus overlooking the processing of cursive words. Manso de Zuniga, Humphreys, and Evett (1991) claimed that there is an early “cursive normalization” encoding stage when processing written words with joined letters. To test this claim, we conducted a lexical decision experiment in which words were presented either with separated or joined letters. To examine if the cost of letter segmentation occurs early in processing, we also manipulated a factor (i.e., word-frequency) that is posited to affect subsequent lexical processing. Results showed faster response times for the words composed of separated letters than for the words composed of joined letters. This effect occurred similarly for low- and high-frequency words. Thus, the present data offer some empirical support to Manso de Zuniga et al.’s (1991) idea of an early “cursive normalization” stage when processing joined-letters words. This pattern of data can be used to constrain the mapping of the visual input into letter and word units in future versions of models of visual word recognition.

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ANIGBOGU, J. C., and A. BELAÏD. "HIDDEN MARKOV MODELS IN TEXT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 06 (December 1995): 925–58. http://dx.doi.org/10.1142/s0218001495000389.

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A multi-level multifont character recognition is presented. The system proceeds by first delimiting the context of the characters. As a way of enhancing system performance, typographical information is extracted and used for font identification before actual character recognition is performed. This has the advantage of sure character identification as well as text reproduction in its original form. The font identification is based on decision trees where the characters are automatically arranged differently in confusion classes according to the physical characteristics of fonts. The character recognizers are built around the first and second order hidden Markov models (HMM) as well as Euclidean distance measures. The HMMs use the Viterbi and the Extended Viterbi algorithms to which enhancements were made. Also present is a majority-vote system that polls the other systems for “advice” before deciding on the identity of a character. Among other things, this last system is shown to give better results than each of the other systems applied individually. The system finally uses combinations of stochastic and dictionary verification methods for word recognition and error-correction.

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Vainio, Seppo, Anneli Pajunen, and Jukka Hyönä. "L1 AND L2 WORD RECOGNITION IN FINNISH." Studies in Second Language Acquisition 36, no. 1 (September 16, 2013): 133–62. http://dx.doi.org/10.1017/s0272263113000478.

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This study investigated the effect of the first language (L1) on the visual word recognition of inflected nouns in second language (L2) Finnish by native Russian and Chinese speakers. Case inflection is common in Russian and in Finnish but nonexistent in Chinese. Several models have been posited to describe L2 morphological processing. The unified competition model (UCM; MacWhinney, 2005) predicts L1-L2 transfer, whereas processability theory (Pienemann, 1998) posits a universal hierarchy in L2 acquisition regardless of the L1. The morphological decomposition deficiency hypothesis (Ullman, 2001b; VanPatten, 2004) claims that nonnatives cannot morphologically decompose words. Finally, DeKeyser (2005) proposes that morphophonological transparency affects nonnative processing. The current study explores which model best accounts for the processing of L2 Finnish by native Russian and Chinese speakers. The materials included simple nouns, transparently inflected nouns, and semitransparently inflected nouns. The results showed that Finns and Russians had longer reaction times (RTs) for morphologically complex nouns, but Chinese had longer RTs for semitransparent nouns. The RT results support the UCM by showing a L1-L2 transfer. Furthermore, transparency influenced word recognition among nonnatives; they made the most errors with semitransparent nouns.

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Journal articles on the topic 'Word recognition models'

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