LDL-AURIS: a computational model, grounded in error-driven learning, for the comprehension of single spoken words

References

• Arnold, D., Tomaschek, F., Sering, K., Lopez, F., & Baayen, R. H. (2017). Words from spontaneous conversational speech can be recognized with human-like accuracy by an error-driven learning algorithm that discriminates between meanings straight from smart acoustic features, bypassing the phoneme as recognition unit. PLOS ONE, 12(4), Article ID e0174623. https://doi.org/10.1371/journal.pone.0174623
   Google Scholar
• Baayen, R. H. (2005). Data mining at the intersection of psychology and linguistics. In A. Cutler (Ed.), Twenty-first century psycholinguistics: Four cornerstones (pp. 69–83). Erlbaum.
   Google Scholar
• Baayen, R. H., Chuang, Y.-Y., & Blevins, J. P. (2018). Inflectional morphology with linear mappings. The Mental Lexicon, 13(2), 230–268. https://doi.org/10.1075/ml
   Web of Science ®Google Scholar
• Baayen, R. H., Chuang, Y.-Y., Shafaei-Bajestan, E., & Blevins, J. (2019). The discriminative lexicon: A unified computational model for the lexicon and lexical processing in comprehension and production grounded not in (de)composition but in linear discriminative learning. Complexity, 2019, Article ID 4895891. https://doi.org/10.1155/2019/4895891
   Google Scholar
• Baayen, R. H., Milin, P., Filipović Durdević, D., Hendrix, P., & Marelli, M. (2011). An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118(3), 438–482. https://doi.org/10.1037/a0023851
   PubMed Web of Science ®Google Scholar
• Baayen, R. H., Milin, P., & Ramscar, M. (2016). Frequency in lexical processing. Aphasiology, 30(11), 1174–1220. https://doi.org/10.1080/02687038.2016.1147767
   Web of Science ®Google Scholar
• Baayen, R. H., Milin, P., Shaoul, C., Willits, J., & Ramscar, M. (2015). Age of first encounter and age of acquisition norms: What raters do when asked the impossible [Manuscript]. University of Tübingen.
   Google Scholar
• Baayen, R. H., Piepenbrock, R., & Gulikers, L. (1995). The CELEX lexical database (CD-ROM). Linguistic Data Consortium, University of Pennsylvania.
   Google Scholar
• Baayen, R. H., Shaoul, C., Willits, J., & Ramscar, M. (2016). Comprehension without segmentation: A proof of concept with naive discriminative learning. Language, Cognition, and Neuroscience, 31(1), 106–128. https://doi.org/10.1080/23273798.2015.1065336
   Web of Science ®Google Scholar
• Baayen, R. H., & Smolka, E. (2020). Modelling morphological priming in German with naive discriminative learning. Frontiers in Communication, Section Language Sciences, 5, Article 17. https://doi.org/10.3389/fcomm.2020.00017
   Google Scholar
• Baayen, R. H., Wurm, L. H., & Aycock, J. (2007). Lexical dynamics for low-frequency complex words: A regression study across tasks and modalities. The Mental Lexicon, 2(3), 419–463. https://doi.org/10.1075/ml.2.3.06baa
   Google Scholar
• Bitterman, M. (2000). Cognitive evolution: A psychological perspective. In C. Heyes & L. Huber (Eds.), The evolution of cognition (pp. 61–79). MIT Press.
   Google Scholar
• Bruni, E., Tran, N.-K., & Baroni, M. (2014). Multimodal distributional semantics. Journal of Artificial Intelligence Research, 49(2014), 1–47. https://doi.org/10.1613/jair.4135
   Google Scholar
• Butterworth, B., (Eds.). (1983). Language production volume 2: Development, writing and other language processes. Academic Press.
   Google Scholar
• Chomsky, N., & Halle, M. (1968). The sound pattern of English. Harper & Row.
   Google Scholar
• Chuang, Y.-Y., Loo, K., Blevins, J. P., & Baayen, R. H. (2019). Estonian case inflection made simple. A case study in Word and Paradigm morphology with linear discriminative learning. PsyArXiv. https://doi.org/10.31234/osf.io/hdftz
   Google Scholar
• Chuang, Y.-Y., Vollmer, M.-L., Shafaei-Bajestan, E., Gahl, S., Hendrix, P., & Baayen, R. H. (2020). The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using linear discriminative learning. Behavior Research Methods, 53(3), 945–679. https://doi.org/10.3758/s13428-020-01356-w
   Web of Science ®Google Scholar
• Cibelli, E. S., Leonard, M. K., Johnson, K., & Chang, E. F. (2015). The influence of lexical statistics on temporal lobe cortical dynamics during spoken word listening. Brain and Language, 147, 66–75. https://doi.org/10.1016/j.bandl.2015.05.005
   PubMed Web of Science ®Google Scholar
• Connine, C. M., Titone, D., & Wang, J. (1993). Auditory word recognition: Extrinsic and intrinsic effects of word frequency. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19(1), 81–94. https://doi.org/10.1037/0278-7393.19.1.81
   PubMed Web of Science ®Google Scholar
• Cooke, M. (2006). A glimpsing model of speech perception in noise. The Journal of the Acoustical Society of America, 119(3), 1562–1573. https://doi.org/10.1121/1.2166600
   PubMed Web of Science ®Google Scholar
• Cucchiarini, C., & Strik, H. (2003). Automatic phonetic transcription: An overview. In Proceedings of the 15th ICPhS (pp. 347–350).
   Google Scholar
• Dahan, D., & Magnuson, J. S. (2006). Chapter 8 – spoken word recognition. In M. J. Traxler & M. A. Gernsbacher (Eds.), Handbook of psycholinguistics (2nd ed., pp. 249–283). Elsevier. https://doi.org/10.1016/B978-012369374-7/50009-2
   Google Scholar
• Danks, D. (2003). Equilibria of the Rescorla-Wagner model. Journal of Mathematical Psychology, 47(2), 109–121. https://doi.org/10.1016/S0022-2496(02)00016-0
   Web of Science ®Google Scholar
• Diehl, R. L., Lotto, A. J., & Holt, L. L. (2004). Speech perception. Annual Review of Psychology, 55(1), 149–179. https://doi.org/10.1146/annurev.psych.55.090902.142028
   PubMedGoogle Scholar
• Ellis, N. C. (2006). Language acquisition as rational contingency learning. Applied Linguistics, 27(1), 1–24. https://doi.org/10.1093/applin/ami038
   Web of Science ®Google Scholar
• Elman, J. L. (2009). On the meaning of words and dinosaur bones: Lexical knowledge without a lexicon. Cognitive Science, 33(4), 547–582. https://doi.org/10.1111/cogs.2009.33.issue-4
   PubMed Web of Science ®Google Scholar
• Ernestus, M. (2000). Voice assimilation and segment reduction in casual Dutch. A corpus-based study of the phonology-phonetics interface. LOT.
   Google Scholar
• Fant, G. (1973). Speech sounds and features. MIT Press.
   Google Scholar
• French, N. R., Carter, C. W., & Koenig, W. (1930). The words and sounds of telephone conversations. The Bell System Technical Journal, 9(2), 290–324. https://doi.org/10.1002/bltj.1930.9.issue-2
   Google Scholar
• Gaskell, M. G., & Marslen-Wilson, W. D. (1997). Integrating form and meaning: A distributed model of speech perception. Language and Cognitive Processes, 12(5-6), 613–656. https://doi.org/10.1080/016909697386646
   Web of Science ®Google Scholar
• Gaskell, M. G., & Marslen-Wilson, W. D. (1999). Ambiguity, competition, and blending in spoken word recognition. Cognitive Science, 23(4), 439–462. https://doi.org/10.1207/s15516709cog2304_3
   Web of Science ®Google Scholar
• Gaskell, M. G., & Marslen-Wilson, W. D. (2001). Lexical ambiguity resolution and spoken word recognition: Bridging the gap. Journal of Memory and Language, 44(3), 325–349. https://doi.org/10.1006/jmla.2000.2741
   Web of Science ®Google Scholar
• Gluck, M. A., & Myers, C. E. (2001). Gateway to memory: An introduction to neural network modeling of the hippocampus and learning. MIT Press.
   Google Scholar
• Hadamard, J. (1908). Mémoire sur le problème d'analyse relatif à l'équilibre des plaques élastiques encastrées. Mémoires présentés par divers savants à l'Académie des sciences de l'Institut de France: Éxtrait. Imprimerie nationale.
   Google Scholar
• Harley, T. A. (2014). The psychology of language: From data to theory (4th ed.). Psychology Press.
   Google Scholar
• Hawkins, S. (2003). Roles and representations of systematic fine phonetic detail in speech understanding. Journal of Phonetics, 31(3-4), 373–405. https://doi.org/10.1016/j.wocn.2003.09.006
   Web of Science ®Google Scholar
• Heitmeier, M., & Baayen, R. H. (2020). Simulating phonological and semantic impairment of English tense inflection with linear discriminative learning. PsyArXiv. https://doi.org/10.31234/osf.io/5eksa
   Google Scholar
• Heitmeier, M., Chuang, Y.-Y., & Baayen, R. H. (2021). Modeling morphology with linear discriminative learning: Considerations and design choices. Preprint. arXiv:2106.07936.
   Google Scholar
• Hendrix, P., Ramscar, M., & Baayen, H. (2019). Ndra: A single route model of response times in the reading aloud task based on discriminative learning. PloS One, 14(7), Article ID e0218802. https://doi.org/10.1371/journal.pone.0218802
   Google Scholar
• Herdan, G. (1960). Type-token mathematics: A textbook of mathematical linguistics (Vol. 4). Mouton.
   Google Scholar
• Hockett, C. F., & Hockett, C. D. (1960). The origin of speech. Scientific American, 203(3), 88–97. https://doi.org/10.1038/scientificamerican0960-88
   PubMed Web of Science ®Google Scholar
• Horata, P., Chiewchanwattana, S., & Sunat, K. (2011). A comparative study of pseudo-inverse computing for the extreme learning machine classifier. The 3rd International Conference on Data Mining and Intelligent Information Technology Applications (pp. 40–45). IEEE.
   Google Scholar
• Ivens, S. H., & Koslin, B. L. (1991). Demands for reading literacy require new accountability methods. Touchstone Applied Science Associates.
   Google Scholar
• Johnson, K. (2004). Massive reduction in conversational American English. In Spontaneous speech: Data and analysis. Proceedings of the 1st session of the 10th international symposium (pp. 29–54). The National International Institute for Japanese Language.
   Google Scholar
• Keuleers, E., Stevens, M., Mandera, P., & Brysbaert, M. (2015). Word knowledge in the crowd: Measuring vocabulary size and word prevalence in a massive online experiment. The Quarterly Journal of Experimental Psychology, 68(8), 1665–1692. https://doi.org/10.1080/17470218.2015.1022560
   PubMed Web of Science ®Google Scholar
• Keune, K., Ernestus, M., Van Hout, R., & Baayen, R. H. (2005). Variation in Dutch: From written MOGELIJK to spoken MOK. Corpus Linguistics and Linguistic Theory, 1(2), 183–223. https://doi.org/10.1515/cllt.2005.1.2.183
   Google Scholar
• Landauer, T. K., & Dumais, S. (1997). A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104(2), 211–240. https://doi.org/10.1037/0033-295X.104.2.211
   Web of Science ®Google Scholar
• Landauer, T. K., Foltz, P. W., & Laham, D. (1998). Introduction to latent semantic analysis. Discourse Processes, 25(2-3), 259–284. https://doi.org/10.1080/01638539809545028
   Web of Science ®Google Scholar
• Licklider, J. C. (1952). On the process of speech perception. The Journal of the Acoustical Society of America, 24(6), 590–594. https://doi.org/10.1121/1.1906938
   Web of Science ®Google Scholar
• Long, R. (2018). Enhancing the TASA corpus for analysis using naive discriminative learning [Unpublished MA Thesis Computational Linguistics, University of Tübingen].
   Google Scholar
• Lu, S.-X., Wang, X., Zhang, G., & Zhou, X. (2015). Effective algorithms of the moore-penrose inverse matrices for extreme learning machine. Intelligent Data Analysis, 19(4), 743–760. https://doi.org/10.3233/IDA-150743
   Web of Science ®Google Scholar
• Luce, P. A., Goldinger, S. D., Auer, E. T., & Vitevitch, M. S. (2000). Phonetic priming, neighborhood activation, and parsyn. Perception & Psychophysics, 62(3), 615–625. https://doi.org/10.3758/BF03212113
   PubMed Web of Science ®Google Scholar
• Magnuson, J. S. (2017). Mapping spoken words to meaning. In M. G. Gaskell & J. Mirkovic (Eds.) Speech perception and spoken word recognition (pp. 76–96). Routledge.
   Google Scholar
• Magnuson, J. S., You, H., Luthra, S., Li, M., Nam, H., Escabi, M., Brown, K., Allopenna, P. D., Theodore, R. M., Monto, N., & Rueckl, J. G. (2020). Earshot: A minimal neural network model of incremental human speech recognition. Cognitive Science, 44(4), Article ID e12823. https://doi.org/10.1111/cogs.12823
   Google Scholar
• Marslen-Wilson, W. D. (1984). Function and process in spoken word recognition. In Attention and performance: Control of language processes (Vol. X, pp. 125–150). Lawrence Erlbaum Associates.
   Google Scholar
• Marslen-Wilson, W. D. (1987). Functional parallelism in spoken word-recognition. Cognition, 25(1-2), 71–102. https://doi.org/10.1016/0010-0277(87)90005-9
   PubMed Web of Science ®Google Scholar
• Marslen-Wilson, W. D., & Welsh, A. (1978). Processing interactions and lexical access during word recognition in continuous speech. Cognitive Psychology, 10(1), 29–63. https://doi.org/10.1016/0010-0285(78)90018-X
   Web of Science ®Google Scholar
• Martinet, A. (1967). Eléments de linguistique générale. Librairie Armand Colin.
   Google Scholar
• McClelland, J. L., & Elman, J. L. (1986). The trace model of speech perception. Cognitive Psychology, 18(1), 1–86. https://doi.org/10.1016/0010-0285(86)90015-0
   PubMed Web of Science ®Google Scholar
• McQueen, J. M. (2005). Speech perception. In K. Lamberts & R. L. Goldstone (Eds.), The handbook of cognition (pp. 255–275). SAGE Publications.
   Google Scholar
• Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In NIPS'13: Proceedings of the 26th International Conference on Neural Information Processing Systems (Vol. 2, pp. 3111–3119). Lake Tahoe, NV: Curran Associates Inc.
   Google Scholar
• Milin, P., Feldman, L. B., Ramscar, M., Hendrix, P., & Baayen, R. H. (2017). Discrimination in lexical decision. PLOS-one, 12(2), Article ID e0171935. https://doi.org/10.1371/journal.pone.0171935
   Google Scholar
• Milin, P., Madabushi, H. T., Croucher, M., & Divjak, D. (2020). Keeping it simple: Implementation and performance of the proto-principle of adaptation and learning in the language sciences. arXiv:2003.03813
   Google Scholar
• Morton, J. (1969). Interaction of information in word recognition. Psychological Review, 76(2), 165–178. https://doi.org/10.1037/h0027366
   Web of Science ®Google Scholar
• Nenadić, F. (2020). Computational modelling of an auditory lexical decision experiment using the discriminative lexicon [PhD thesis, University of Alberta]. https://doi.org/10.7939/r3-whrd-a130
   Google Scholar
• Nixon, J. S. (2020). Of mice and men: Speech sound acquisition as discriminative learning from prediction error, not just statistical tracking. Cognition, 197, Article ID 104081. https://doi.org/10.1016/j.cognition.2019.104081
   Google Scholar
• Nixon, J. S., & Tomaschek, F. (2021). Prediction and error in early infant speech learning: A speech acquisition model. Cognition, 212, Article ID 104697. https://doi.org/10.1016/j.cognition.2021.104697
   Google Scholar
• Nixon, J. S., & Tomaschek, F. (2020). Learning from the acoustic signal: Error-driven learning of low-level acoustics discriminates vowel and consonant pairs. In 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020): Developing a Mind: Learning in Humans, Animals, and Machines (pp. 585–591). Red Hook, NY: Curran Associates, Inc.
   Google Scholar
• Norris, D. (1994). Shortlist: A connectionist model of continuous speech recognition. Cognition, 52(3), 189–234. https://doi.org/10.1016/0010-0277(94)90043-4
   Web of Science ®Google Scholar
• Norris, D., & McQueen, J. (2008). Shortlist B: A Bayesian model of continuous speech recognition. Psychological Review, 115(2), 357–395. https://doi.org/10.1037/0033-295X.115.2.357
   PubMed Web of Science ®Google Scholar
• Olejarczuk, P., Kapatsinski, V., & Baayen, R. H. (2018). Distributional learning is error-driven: The role of surprise in the acquisition of phonetic categories. Linguistic Vanguard, 4(s2). https://doi.org/10.1515/lingvan-2017-0020
   Google Scholar
• O'Shaughnessy, D. (1987). Speech communications: Human and machine (IEEE). Universities press.
   Google Scholar
• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12(85), 2825–2830.
   Google Scholar
• Penrose, R. (1955). A generalized inverse for matrices. Mathematical Proceedings of the Cambridge Philosophical Society, 51(3), 406–413. https://doi.org/10.1017/S0305004100030401
   Google Scholar
• Phillips, C. (2001). Levels of representation in the electrophysiology of speech perception. Cognitive Science, 25(5), 711–731. https://doi.org/10.1207/s15516709cog2505_5
   Web of Science ®Google Scholar
• Pickett, J., & Pollack, I. (1963). Intelligibility of excerpts from fluent speech: Effects of rate of utterance and duration of excerpt. Language and Speech, 6(3), 151–164. https://doi.org/10.1177/002383096300600304
   Web of Science ®Google Scholar
• Pisoni, D. B., & Luce, P. A. (1987). Acoustic-phonetic representations in word recognition. Cognition, 25(1-2), 21–52. https://doi.org/10.1016/0010-0277(87)90003-5
   PubMed Web of Science ®Google Scholar
• Pitt, M., Johnson, K., Hume, E., Kiesling, S., & Raymond, W. (2005). The Buckeye corpus of conversational speech: Labeling conventions and a test of transcriber reliability. Speech Communication, 45(1), 89–95. https://doi.org/10.1016/j.specom.2004.09.001
   Web of Science ®Google Scholar
• Plag, I., Homann, J., & Kunter, G. (2017). Homophony and morphology: The acoustics of word-final S in English. Journal of Linguistics, 53(1), 181–216. https://doi.org/10.1017/S0022226715000183
   Web of Science ®Google Scholar
• Pollack, I., & Pickett, J. (1963). The intelligibility of excerpts from conversation. Language and Speech, 6(3), 165–171. https://doi.org/10.1177/002383096300600305
   Web of Science ®Google Scholar
• Port, R. F., & Leary, A. P. (2005). Against formal phonology. Language, 81(4), 927–964. https://doi.org/10.1353/lan.2005.0195
   Web of Science ®Google Scholar
• Ramscar, M. (2013). Suffixing, prefixing, and the functional order of regularities in meaningful strings. Psihologija, 46(4), 377–396. https://doi.org/10.2298/PSI1304377R
   Web of Science ®Google Scholar
• Ramscar, M., Dye, M., & McCauley, S. M. (2013). Error and expectation in language learning: The curious absence of mouses in adult speech. Language, 89(4), 760–793. https://doi.org/10.1353/lan.2013.0068
   Web of Science ®Google Scholar
• Ramscar, M., Hendrix, P., Shaoul, C., Milin, P., & Baayen, R. H. (2014). Nonlinear dynamics of lifelong learning: The myth of cognitive decline. Topics in Cognitive Science, 6(1), 5–42. https://doi.org/10.1111/tops.12078
   PubMed Web of Science ®Google Scholar
• Ramscar, M., & Yarlett, D. (2007). Linguistic self-correction in the absence of feedback: A new approach to the logical problem of language acquisition. Cognitive Science, 31(6), 927–960. https://doi.org/10.1080/03640210701703576
   PubMed Web of Science ®Google Scholar
• Ramscar, M., Yarlett, D., Dye, M., Denny, K., & Thorpe, K. (2010). The effects of feature-label-order and their implications for symbolic learning. Cognitive Science, 34(6), 909–957. https://doi.org/10.1111/(ISSN)1551-6709
   PubMed Web of Science ®Google Scholar
• Rescorla, R. A. (1988). Pavlovian conditioning: It's not what you think it is. American Psychologist, 43(3), 151–160. https://doi.org/10.1037/0003-066X.43.3.151
   PubMed Web of Science ®Google Scholar
• Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In A. H. Black & W. F. Prokasy (Eds.), Classical conditioning II: Current research and theory (pp. 64–99). New York: Appleton-Century-Crofts.
   Google Scholar
• Scharenborg, O. (2008). Modelling fine-phonetic detail in a computational model of word recognition. In Proceedings of interspeech 2008 (pp. 1473–1476). Brisbane: Causal Productions Pty Ltd.
   Google Scholar
• Scharenborg, O. (2009). Using durational cues in a computational model of spoken-word recognition. In Proceedings of interspeech 2009 (pp. 1675–1678). ISCA Archive.
   Google Scholar
• Scharenborg, O., & Boves, L. (2010). Computational modelling of spoken-word recognition processes: Design choices and evaluation. Pragmatics & Cognition, 18(1), 136–164. https://doi.org/10.1075/pc
   Web of Science ®Google Scholar
• Seidenberg, M. S., & McClelland, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96(4), 523–568. https://doi.org/10.1037/0033-295X.96.4.523
   PubMed Web of Science ®Google Scholar
• Sering, K., Milin, P., & Baayen, R. H. (2018). Language comprehension as a multi-label classification problem. Statistica Neerlandica, 72(3), 339–353. https://doi.org/10.1111/stan.12134
   Web of Science ®Google Scholar
• Sering, K., Weitz, M., Kuenstle, D., & Schneider, L. (2020). Pyndl: Naive discriminative learning in python. https://doi.org/10.5281/zenodo.1134829
   Google Scholar
• Shafaei-Bajestan, E., & Baayen, R. H. (2018). Wide learning for auditory comprehension. In B. Yegnanarayana (Ed.), Proceedings of interspeech 2018 (pp. 966–970). International Speech Communication Association (ISCA).
   Google Scholar
• Shahmohammadi, H., Lensch, H., & Baayen, R. H. (2021). Learning zero-shot multifaceted visually grounded word embeddings via multi-task training. Preprint. arXiv:2104.07500
   Google Scholar
• Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1–48. https://doi.org/10.1186/s40537-019-0197-0
   Google Scholar
• Siegel, S., & Allan, L. G. (1996). The widespread influence of the rescorla-wagner model. Psychonomic Bulletin & Review, 3(3), 314–321. https://doi.org/10.3758/BF03210755
   PubMed Web of Science ®Google Scholar
• ten Bosch, L., Boves, L., Tucker, B., & Ernestus, M. (2015). Diana: Towards computational modeling reaction times in lexical decision in North American English. In Interspeech 2015: 16th annual conference of the international speech communication association (pp. 1576–1580). International Speech Communication Association.
   Google Scholar
• Tomaschek, F., Plag, I., Ernestus, M., & Baayen, R. H. (2019). Modeling the duration of word-final s in English with naive discriminative learning. Journal of Linguistics, 57(1), 123–161. https://doi.org/10.1017/S0022226719000203
   Web of Science ®Google Scholar
• Tomasello, M. (2003). Constructing a language: A usage-based theory of language acquisition. Harvard University Press.
   Google Scholar
• Tucker, B. V., Brenner, D., Danielson, K., Kelley, M. C., Nenadić, F., & Sims, M. (2019). The massive auditory lexical decision database: Toward reliable, generalizable speech research. Behavior Research Methods, 51(3), 1187–1204. https://doi.org/10.3758/s13428-018-1056-1
   PubMed Web of Science ®Google Scholar
• Uhrig, P. (2018a). Newsscape and the distributed little red hen lab – a digital infrastructure for the large-scale analysis of tv broadcasts. In A.-J. Zwierlein, J. Petzold, K. Böhm, & M. Decker (Eds.), Anglistentag 2017 in Regensburg: Proceedings. Proceedings of the conference of the German association of university teachers of English (pp. 99–114). Wissenschaftlicher Verlag Trier.
   Google Scholar
• Uhrig, P. (2018b). Subjects in English: From valency grammar to a constructionist treatment of non-canonical subjects. De Gruyter Mouton.
   Google Scholar
• Uhrig, P. (2020). Mutltimodal research in linguistics. Zeitschrift für Anglistik und Amerikanistik, 68(4), 1–5. https://doi.org/10.1515/zaa-2020-2019
   Web of Science ®Google Scholar
• Uhrig, P. (2021). Large-scale multimodal corpus linguistics: The big data turn [Postdoctoral Thesis (Habilitation), FAU Erlangen-Nürnberg].
   Google Scholar
• van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9(86), 2579–2605.
   Google Scholar
• Warren, R. M. (1970). Perceptual restoration of missing speech sounds. Science, 167(3917), 392–393. https://doi.org/10.1126/science.167.3917.392
   PubMed Web of Science ®Google Scholar
• Warren, R. M. (1971). Identification times for phonemic components of graded complexity and for spelling of speech. Perception & Psychophysics, 9(4), 345–349. https://doi.org/10.3758/BF03208692
   Web of Science ®Google Scholar
• Warren, R. M. (2000). Phonemic organization does not occur: Hence no feedback. Behavioral and Brain Sciences, 23(3), 350–325. https://doi.org/10.1017/S0140525X00503240
   Web of Science ®Google Scholar
• Weber, A., & Scharenborg, O. (2012). Models of spoken-word recognition. Wiley Interdisciplinary Reviews: Cognitive Science, 3(3), 387–401. https://doi.org/10.1002/wcs.1178
   PubMed Web of Science ®Google Scholar
• Weitz, M. (2019). Balancing bias in natural language recognition using LSTMs [Unpublished Lab Rotation Report at Quantitative Linguistics Group]. University of Tübingen.
   Google Scholar
• Widrow, B., & Hoff, M. E. (1960, August 23–26). Adaptive switching circuits. In 1960 IRE WESCON Convention Record: At the Western Electronic Show and Convention, Los Angeles, CA (Vol. 4, pp. 96–104). Institute of Radio Engineers.
   Google Scholar
• Wood, S. N. (2017). Generalized additive models. Chapman & Hall/CRC.
   Google Scholar
• Zhang, D., & Yang, Z. (2018). Word embedding perturbation for sentence classification. Preprint. arXiv:1804.08166
   Google Scholar
• Zhang, X., & Samuel, A. G. (2015). The activation of embedded words in spoken word recognition. Journal of Memory and Language, 79–80, 53–75. https://doi.org/10.1016/j.jml.2014.12.001
   PubMed Web of Science ®Google Scholar