Making long-distance relationships work: Quantifying lexical competition with Hidden Markov Models

[1]  G. A. Miller,et al.  An Analysis of Perceptual Confusions Among Some English Consonants , 1955 .

[2]  H. Savin Word‐Frequency Effect and Errors in the Perception of Speech , 1963 .

[3]  E Foulke,et al.  Listening comprehension as a function of word rate. , 1968, The Journal of communication.

[4]  P. D. Eimas,et al.  Selective adaptation of linguistic feature detectors , 1973 .

[5]  Miss A.O. Penney (b) , 1974, The New Yale Book of Quotations.

[6]  James L. McClelland,et al.  The TRACE model of speech perception , 1986, Cognitive Psychology.

[7]  W. Marslen-Wilson Functional parallelism in spoken word-recognition , 1987, Cognition.

[8]  S. Andrews Frequency and neighborhood effects on lexical access: Activation or search? , 1989 .

[9]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[10]  S. Goldinger,et al.  Priming Lexical Neighbors of Spoken Words: Effects of Competition and Inhibition. , 1989, Journal of memory and language.

[11]  D. Norris Shortlist: a connectionist model of continuous speech recognition , 1994, Cognition.

[12]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[13]  David B. Pisoni,et al.  Lexical Effects on Spoken Word Recognition by Pediatric Cochlear Implant Users , 1995, Ear and hearing.

[14]  M. Sommers The structural organization of the mental lexicon and its contribution to age-related declines in spoken-word recognition. , 1995, Psychology and aging.

[15]  Mitchell S. Sommers,et al.  The structural organization of the mental lexicon and its contribution to age-related declines in spoken-word recognition. , 1996 .

[16]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  P. Luce,et al.  When Words Compete: Levels of Processing in Perception of Spoken Words , 1998 .

[18]  Paul D. Allopenna,et al.  Tracking the Time Course of Spoken Word Recognition Using Eye Movements: Evidence for Continuous Mapping Models , 1998 .

[19]  D. Pisoni,et al.  Recognizing Spoken Words: The Neighborhood Activation Model , 1998, Ear and hearing.

[20]  James H. Martin,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.

[21]  S. Goldinger,et al.  Phonetic priming, neighborhood activation, and PARSYN , 2000, Perception & psychophysics.

[22]  J. S. Long,et al.  Regression models for categorical dependent variables using Stata, 2nd Edition , 2005 .

[23]  M. Tanenhaus,et al.  Time Course of Frequency Effects in Spoken-Word Recognition: Evidence from Eye Movements , 2001, Cognitive Psychology.

[24]  E. T. Auer The influence of the lexicon on speech read word recognition: Contrasting segmental and lexical distinctiveness , 2002, Psychonomic bulletin & review.

[25]  M. Tanenhaus,et al.  The time course of spoken word learning and recognition: studies with artificial lexicons. , 2003, Journal of experimental psychology. General.

[26]  M. K. Pickora-Fuller Processing speed and timing in aging adults: psychoacoustics, speech perception, and comprehension. , 2003, International journal of audiology.

[27]  John W. Senders,et al.  Frequency and neighborhood effects on auditory perception of drug names in noise , 2005 .

[28]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[29]  Ronald Christensen,et al.  GRAPHICAL VIEWS OF SUPPRESSION AND MULTICOLLINEARITY IN MULTIPLE LINEAR REGRESSION , 2006 .

[30]  Odette Scharenborg,et al.  Reaching over the gap: A review of efforts to link human and automatic speech recognition research , 2007, Speech Commun..

[31]  Mark J. F. Gales,et al.  The Application of Hidden Markov Models in Speech Recognition , 2007, Found. Trends Signal Process..

[32]  Rebecca Treiman,et al.  The English Lexicon Project , 2007, Behavior research methods.

[33]  R. Harald Baayen,et al.  Analyzing linguistic data: a practical introduction to statistics using R, 1st Edition , 2008 .

[34]  R. Baayen,et al.  Mixed-effects modeling with crossed random effects for subjects and items , 2008 .

[35]  D. Balota,et al.  Moving beyond Coltheart’s N: A new measure of orthographic similarity , 2008, Psychonomic bulletin & review.

[36]  Marc Brys,et al.  Moving beyond Kučera and Francis: A critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English , 2009 .

[37]  Byung-Jun Yoon,et al.  Hidden Markov Models and their Applications in Biological Sequence Analysis , 2009, Current genomics.

[38]  Austin F. Frank,et al.  Analyzing linguistic data: a practical introduction to statistics using R , 2010 .

[39]  Clement T. Yu,et al.  Listen carefully: the risk of error in spoken medication orders. , 2010, Social science & medicine.

[40]  Lidia Suárez,et al.  Observing neighborhood effects without neighbors , 2011, Psychonomic bulletin & review.

[41]  Mitchell Sommers,et al.  There goes the neighborhood: Lipreading and the structure of the mental lexicon , 2011, Speech Commun..

[42]  D. Mirman,et al.  Competition and cooperation among similar representations: toward a unified account of facilitative and inhibitory effects of lexical neighbors. , 2012, Psychological review.

[43]  Harlan D. Harris,et al.  Computational Models of SWR , 2011 .

[44]  D. Barr,et al.  Random effects structure for confirmatory hypothesis testing: Keep it maximal. , 2013, Journal of memory and language.

[45]  D. Pisoni,et al.  Misperceptions of spoken words: data from a random sample of American English words. , 2013, The Journal of the Acoustical Society of America.

[46]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[47]  R. Harald Baayen,et al.  The Myth of Cognitive Decline: Non-Linear Dynamics of Lifelong Learning , 2014, Top. Cogn. Sci..

[48]  Lee H. Wurm,et al.  What residualizing predictors in regression analyses does (and what it does not do) , 2014 .

[49]  Julia F Strand,et al.  Phi-square Lexical Competition Database (Phi-Lex): An online tool for quantifying auditory and visual lexical competition , 2014, Behavior research methods.

[50]  Vannacci Lorenzo Competition and Cooperation in Rail and Air , 2014 .

[51]  Qi Chen,et al.  Interaction Between Phonological and Semantic Representations: Time Matters , 2015, Cogn. Sci..

[52]  Joseph Slote,et al.  Conducting spoken word recognition research online: Validation and a new timing method , 2016, Behavior research methods.

[53]  M. Próchniak,et al.  The Application of Hidden Markov Models to the Analysis of Real Convergence , 2017 .

[54]  Jianxin Wu Hidden Markov model , 2018 .