Citations & References¶

PhonoLex integrates a CMU-grounded lexicon, in-house psycholinguistic norm derivations, learned phoneme feature vectors, a Qwen3-Embedding-derived word-similarity graph, and a curated naturalistic English corpus. Original-author papers are cited as scale anchors for the in-house derivations — the per-word values served by PhonoLex are PhonoLex's, validated against held-out oracles where applicable. See Data & Methods for full provenance per column.

Pronunciation data¶

CMU Pronouncing Dictionary¶

Carnegie Mellon University. (2014). The CMU Pronouncing Dictionary (~134,000 words). License: Modified BSD. http://www.speech.cs.cmu.edu/cgi-bin/cmudict

In-house psycholinguistic norms¶

PhonoLex norms are derived locally via gpt-4.1-mini cloze-prompt ratings over ~47K non-PROPN content words. Methodology adapts the published scales referenced below. Per-word values served by PhonoLex are not redistributions of the original CSVs.

Methodology validation¶

The LLM-cloze approach is grounded in two recent validation papers showing that LLM-derived psycholinguistic ratings correlate r = .74–.95 with human raters across multiple norm sets, and outperform human ratings on downstream prediction tasks — establishing LLM-cloze as a viable replacement for redistributing human-rated norm CSVs:

Martínez, G., Conde, J., Reviriego, P., & Brysbaert, M. (2025). Using Large Language Models to Generate Psycholinguistic Norms. Behavior Research Methods (in press).
Brysbaert, M. (2024). Validating LLM-derived psycholinguistic ratings for word stimuli. Memory & Cognition.

Affective (valence + arousal)¶

PhonoLex in-house derivation. Scale anchor: Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, 45(4), 1191–1207. DOI: 10.3758/s13428-012-0314-x. Validated against held-out Warriner oracle (Spearman 0.836 vs valence on N=500 pilot). The Warriner D (dominance) axis was not re-derived.

Age of acquisition¶

PhonoLex in-house derivation: gpt-4.1-mini cloze with logprob expected-value extraction over a 1-7 scale anchored to age bands (1 = 0-2 yrs, 2 = 3-4, 3 = 5-6, 4 = 7-8, 5 = 9-10, 6 = 11-12, 7 = 13 yrs+). Validated Spearman 0.868 vs Glasgow Norms (N=5,551), Pearson 0.816 vs Kuperman on N=500 Glasgow-unseen rows.

Validation oracle: Scott, G. G., Keitel, A., Becirspahic, M., Yao, B., & Sereno, S. C. (2019). The Glasgow Norms: Ratings of 5,500 words on nine scales. Behavior Research Methods, 51(3), 1258–1270. DOI: 10.3758/s13428-018-1099-3 (AOA.M column, CC BY 4.0).

Secondary cross-construct oracle (not redistributed): Kuperman, V., Stadthagen-Gonzalez, H., & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior Research Methods, 44(4), 978–990. DOI: 10.3758/s13428-012-0210-4.

Concreteness, familiarity, imageability¶

PhonoLex in-house derivation. Scale anchors: - Brysbaert, M., Warriner, A. B., & Kuperman, V. (2014). Concreteness ratings for 40 thousand generally known English word lemmas. Behavior Research Methods, 46(3), 904–911. DOI: 10.3758/s13428-013-0403-5 - Scott, G. G., Keitel, A., Becirspahic, M., Yao, B., & Sereno, S. C. (2019). The Glasgow Norms: Ratings of 5,500 words on nine scales. Behavior Research Methods, 51(3), 1258–1270. DOI: 10.3758/s13428-018-1099-3 (imageability + familiarity)

Body-Object Interaction (BOI)¶

PhonoLex in-house derivation. Scale anchor: Pexman, P. M., Muraki, E., Sidhu, D. M., Siakaluk, P. D., & Yap, M. J. (2019). Quantifying sensorimotor experience: Body-object interaction ratings for more than 9,000 English words. Behavior Research Methods, 51(2), 453–466. DOI: 10.3758/s13428-018-1171-z

Iconicity¶

PhonoLex in-house derivation. Scale anchor: Winter, B., Lupyan, G., Perry, L. K., Dingemanse, M., & Perlman, M. (2024). Iconicity ratings for 14,000+ English words. Behavior Research Methods, 56(3), 1640–1655. DOI: 10.3758/s13428-023-02112-6

Socialness¶

PhonoLex in-house derivation. Scale anchor: Diveica, V., Pexman, P. M., & Binney, R. J. (2023). Quantifying social semantics: An inclusive definition of socialness and ratings for 8,388 English words. Behavior Research Methods, 55(2), 461–473. DOI: 10.3758/s13428-022-01810-x

Semantic Diversity¶

PhonoLex in-house derivation. Scale anchor: Hoffman, P., Lambon Ralph, M. A., & Rogers, T. T. (2013). Semantic diversity: A measure of semantic ambiguity based on variability in the contextual usage of words. Behavior Research Methods, 45(3), 718–730. DOI: 10.3758/s13428-012-0278-x

Lexical frequency¶

General-corpus frequency (FineWeb-Edu)¶

PhonoLex in-house derivation from HuggingFaceFW/fineweb-edu (~800M tokens / 1M docs). License: ODC-BY 1.0. https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu

Penedo, G., Kydlíček, H., Lozhkov, A., et al. (2024). FineWeb-Edu: an open and high-quality dataset for educational content.

Children and Young People's Books Lexicon (CYP-LEX)¶

Korochkina, M., Marelli, M., Brysbaert, M., & Rastle, K. (2024). The Children and Young People's Books Lexicon (CYP-LEX): A large-scale lexical database of books read by children and young people in the United Kingdom. Quarterly Journal of Experimental Psychology, 77(11), 2197–2214. License: CC BY 4.0. https://osf.io/squ49/

Developmental frequency (CHILDES + PhonBank, child PRODUCTION)¶

PhonoLex (PhonBank) + (CHILDES). Source corpora:

MacWhinney, B. (2000). The CHILDES Project: Tools for Analyzing Talk (3rd ed.). Lawrence Erlbaum Associates. https://childes.talkbank.org/

Rose, Y., & MacWhinney, B. (2014). The PhonBank Project: Data and software-assisted methods for the study of phonology and phonological development. In J. Durand, U. Gut, & G. Kristoffersen (Eds.), The Oxford Handbook of Corpus Phonology. https://phonbank.talkbank.org/

Phonological theory + learned features¶

Theory-assigned features (Bayesian prior)¶

Hayes, B. (2009). Introductory Phonology. Wiley-Blackwell. (Used as prior for the 26-d Bayesian phoneme feature inference in packages/features/.)

Moisik, S. R., & Esling, J. H. (2011). The 'whole larynx' approach to laryngeal features. Proceedings of the International Congress of Phonetic Sciences XVII, 1406–1409.

Perceptual confusion (Bayesian evidence + word-similarity edges)¶

Marxer, R., Barker, J., Martin, N., & Coleman, J. (2016). Modelling speech intelligibility in adverse conditions: a corpus study (Edinburgh Closed-set Confusability Corpus / ECCC v1.2). License: CC BY 4.0. https://datashare.ed.ac.uk/handle/10283/2791

Vowel acoustics (Bayesian evidence for vowel posteriors)¶

Hillenbrand, J., Getty, L. A., Clark, M. J., & Wheeler, K. (1995). Acoustic characteristics of American English vowels. Journal of the Acoustical Society of America, 97(5 Pt 1), 3099–3111. DOI: 10.1121/1.411872

Similarity algorithm¶

Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics Doklady, 10(8), 707–710.

Word complexity¶

Stoel-Gammon, C. (2010). The Word Complexity Measure: Description and application to developmental phonology and disorders. Clinical Linguistics & Phonetics, 24(4-5), 271–282. DOI: 10.3109/02699200903581059

Phonotactic probability¶

Vitevitch, M. S., & Luce, P. A. (2004). A Web-based interface to calculate phonotactic probability for words and nonwords in English. Behavior Research Methods, Instruments, & Computers, 36(3), 481–487. DOI: 10.3758/BF03195594. (Method origin; PhonoLex computes values directly from the CMU dict.)

Word similarity graph¶

Qwensim (PhonoLex )¶

PhonoLex in-house derivation: ~1.6M word-similarity edges from Qwen3-Embedding-0.6B cosine over FineWeb-Edu. Bulk of the similarity graph (~99.8%). Honest framing: sentence-transformer semantic similarity, not free-association norm data.

https://huggingface.co/Qwen/Qwen3-Embedding-0.6B

ECCC (small perceptual-confusability layer)¶

Marxer et al. (2016) — see phonological theory section above. ~2.5K edges.

WordSim-353 (small human-rated similarity layer)¶

Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., & Ruppin, E. (2001). Placing search in context: The concept revisited (WordSim-353). Proceedings of the 10th International Conference on World Wide Web, 406–414. DOI: 10.1145/371920.372094. ~351 edges.

Morphology¶

Batsuren, K., Bella, G., & Giunchiglia, F. (2021). MorphyNet: a Large Multilingual Database of Derivational and Inflectional Morphology. Proceedings of the 18th SIGMORPHON Workshop, 39–48. License: CC BY-SA 3.0. https://github.com/kbatsuren/MorphyNet

Sentences corpus¶

The Sentences tool draws from ~236K naturalistic English sentences across the following sources:

Warstadt, A., Singh, A., & Bowman, S. R. (2019). Neural Network Acceptability Judgments (CoLA — Corpus of Linguistic Acceptability, positive examples). Transactions of the ACL. https://nyu-mll.github.io/CoLA/

Nivre, J., et al. (2020). Universal Dependencies v2 (UD English-EWT + GUM corpora). License: CC BY-SA. https://universaldependencies.org/

Tatoeba contributors. (2024). Tatoeba sentence collection (English subset). License: CC BY 2.0 FR. https://tatoeba.org/

Lison, P., & Tiedemann, J. (2016). OpenSubtitles2018: Statistical Rescoring of Sentence Alignments in Large, Noisy Parallel Corpora. Proceedings of LREC. http://www.opensubtitles.org/

CHILDES + PhonBank conversational transcripts were retired from the Sentences corpus 2026-05-25 (CHAT-transcript artifacts unfit for SLP material); they remain in use for the developmental-frequency derivations above.

Clinical intervention approaches¶

Maximal opposition¶

Gierut, J. A. (1989). Maximal opposition approach to phonological treatment. Journal of Speech and Hearing Disorders, 54(1), 9–19. DOI: 10.1044/jshd.5401.09

Gierut, J. A. (1990). Differential learning of phonological oppositions. Journal of Speech and Hearing Research, 33(3), 540–549. DOI: 10.1044/jshr.3303.540

Gierut, J. A., & Neumann, H. J. (1992). Teaching and learning /θ/: A non-confound. Clinical Linguistics & Phonetics, 6(3), 191–200. DOI: 10.3109/02699209208985533

Multiple opposition¶

Williams, A. L. (2000). Multiple oppositions: theoretical foundations for an alternative contrastive intervention approach. American Journal of Speech-Language Pathology, 9(4), 282–288. DOI: 10.1044/1058-0360.0904.282

Comparative review¶

Storkel, H. L. (2022). Minimal, Maximal, or Multiple: Which Contrastive Intervention Approach to Use With Children With Speech Sound Disorders? Language, Speech, and Hearing Services in Schools, 53(3), 632–645. DOI: 10.1044/2022_LSHSS-21-00137