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Letters hide the truth from our eyes: English homophones have meaningfully different phonetic realizations

Paper · 2026 · Yu-Hsiang Tseng, Mirjam T. C. Ernestus, Louis F. M. ten Bosch et al.

Summary

The paper asks whether English heterographic homophones such as wait and weight are pronounced identically. Earlier work showed only that their durations differ with frequency and meaning. The authors take about 14,000 homophone tokens from 35 pairs in American TV news recordings (the Redhen resource). They represent each token as a time-normalized spectrogram rather than a phonetic transcription. Within the Discriminative Lexicon Model, they test whether the acoustic form of a token can be predicted from its contextualized meaning, using Linear Discriminative Learning (LDL) mappings between text-derived embeddings and spectrogram features.

The two members of a pair turn out to differ systematically in their acoustics, and these differences follow from meaning in context. The effect survives once duration-related variation is removed with a PLS-based deflation procedure. The findings argue that meaning and form are more tightly coupled than formal phonology and layered lexicon models allow. They also suggest that symbolic transcriptions can mask real phonetic detail.

Key points

  • Data: about 14,000 tokens of 35 English homophone pairs, drawn from American television news broadcasts in the Redhen resource (Joo et al., 2017).
  • Theoretical frame: Heitmeier et al.'s (2026) Discriminative Lexicon Model, in which LDL production and comprehension routes connect semantic embeddings (S) and speech vectors (C) through a least-squares matrix G.
  • Time-normalized spectrograms replace phonetic transcriptions as the representation of a word's realization.
  • Duration control: PLS components that predict duration are extracted until the remaining matrix (C_defl) predicts duration no better than a permutation baseline.
  • In the LDL production model, the prediction accounts for about .43 of the Frobenius-norm variance and the residual for about .56.
    • Within the prediction, the duration part is only .08 and the non-duration part is .35.
    • So most of the meaning-driven signal is not about duration.

Interesting ideas

  • I1. Iteratively deflate a feature matrix by the components that predict a confound, stop when prediction falls to a permutation baseline, then check whether the effect of interest still holds; this isolates a signal from an obvious nuisance variable
  • I2. Because least-squares predictions are orthogonal to their residuals, the squared Frobenius norm splits cleanly into an "explained" part and a "residual" part, giving an R²-like score for matrix-to-matrix mappings
  • I3. Splitting both the prediction and the residual into a confound part and an "other" part shows where the confound actually enters the model (through semantics versus outside it)
  • I4. Skipping a lossy symbolic layer (transcriptions) in favor of a dense, normalized raw signal exposed differences the symbols erased
  • I5. Treating identical-form items as a controlled experiment, where form is fixed and only meaning varies, is a clean way to measure how much context alone determines behavior

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): when evaluating whether a parser or frame feature truly carries meaning, deflate away sentence length or token frequency first and keep the permutation baseline as a negative control.
  • I2 (world): this yields a simple, explainable measure of how much of a knowledge-graph or embedding space is accounted for by compiled verb classes or ontology features versus left unexplained.
  • I3 (learn): failure analysis of witness-parser disagreements could separate errors traceable to surface factors from errors traceable to meaning, guiding which repair rules to induce.
  • I4 (tokens): keep lemma and tag as explainable labels, but also store context features per token, so that items collapsed by one spelling or lemma (homographs, homophone misspellings like wait/weight) can still be told apart.
  • I5 (answer): build test sets of minimal pairs that share a surface form but differ in meaning, to check that readers answer from the frame rather than from the string.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of TübingenTübingen · Germany75 works · 41,081 citations🎓Radboud University NijmegenNijmegen · Netherlands20 works · 1,688 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksPsychology · 1,552 worksExperimental and Cognitive Psychology · 870 works

🧭 Topics 1Research subjects and languages the work is about.English · 2,263 works

Cite

Cite as Yu-Hsiang Tseng, Mirjam T. C. Ernestus, Louis F. M. ten Bosch and R. Harald Baayen. 2026. Letters hide the truth from our eyes: English homophones have meaningfully different phonetic realizations. arXiv:2608.26749.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2608.26749 · arXiv 2608.26749

@misc{tseng2026letters,
  title = {{Letters hide the truth from our eyes: English homophones have meaningfully different phonetic realizations}},
  author = {Yu-Hsiang Tseng, Mirjam T. C. Ernestus, Louis F. M. {ten Bosch} and R. Harald Baayen},
  year = {2026},
  doi = {10.48550/arxiv.2608.26749},
  eprint = {2608.26749},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2608.26749}
}

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