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REVIEW 3 major objections 6 minor 14 references

Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A single rich Persian dataset lifts homograph accuracy by roughly 30 points in both neural and rule-based G2P systems.

desk verdict A genuinely useful Persian homograph dataset and a plausibly large gain for rule-based G2P, but the self-authored benchmark keeps the headline numbers from being fully trustworthy. read the letter →

arxiv 2505.12973 v1 pith:TWKVZ3SQ submitted 2025-05-19 cs.CL

Mahta Fetrat Qharabagh , Zahra Dehghanian , Hamid R. Rabiee This is my paper · ORCID
classification cs.CL
keywords Grapheme-to-phonemeconversionhomographdisambiguationPersianlow-resourcelanguagesrule-basedG2Ptext-to-speechaccessibilityLLM-generateddata
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Homograph disambiguation—choosing the right pronunciation for words spelled the same—is the weak point of Persian grapheme-to-phoneme (G2P) systems, and the systems that need it most, like screen readers, cannot afford slow neural models. This paper argues that a large, balanced, openly licensed homograph dataset can fix both problems at once: it reports HomoRich, 528,891 phonemized Persian sentences covering 285 homographs, built with a semi-automated pipeline that combines human-written examples, LLM-generated sentences, and public corpora. Fine-tuning the neural G2P model GE2PE on this data raises homograph accuracy from 47.17% to 76.89%. Adding a lightweight statistical context-overlap module to the rule-based eSpeak engine yields HomoFast eSpeak, which reaches 74.53% homograph accuracy while remaining the fastest tool tested—evidence that fast rule-based G2P can be upgraded with rich offline data rather than replaced.

What carries the argument

The load-bearing mechanism is the HomoRich dataset itself, built by filtering a Persian pronunciation dictionary for 285 homographs and generating balanced, context-diverse sentences; that dataset feeds a per-pronunciation context-word database with normalized weighted-overlap scoring, a statistical disambiguator with no neural components or embeddings. A second mechanism is the LLM-powered phonemization pipeline—prompting GPT-4o with Finglish transcriptions, few-shot examples, and dictionary hints—which turns raw sentences into phoneme labels at 6.43% phoneme error rate and 64% homograph accuracy, cheaply enough to make large-scale homograph data feasible in a low-resource language.

What would settle it

Take a fresh set of Persian sentences containing the same 285 homographs, have native speakers annotate the intended pronunciations by hand (no LLM phonemes, no prior exposure to HomoRich), and run Homo-GE2PE, HomoFast eSpeak, and their baselines on it; if the ~30-point accuracy gap shrinks toward zero or the improved models fall below random accuracy on this independent set, the central claim fails.

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Extended reading notes

Core claim

The paper's central claim is that data quality can substitute for model complexity in homograph disambiguation: the same curated dataset that pushes a neural model to 76.89% homograph accuracy also lets a purely statistical, embedding-free module inside eSpeak reach 74.53%, a 30.66-point gain over eSpeak's 43.87%, with no loss of speed. The mechanism is a per-pronunciation database of context words built from HomoRich; for each incoming sentence the system scores the overlap between its content words and each pronunciation's context list, normalizes by list length, and picks the pronunciation with the highest score. The authors report the two improved systems—Homo-GE2PE and HomoFast eSpeak—as direct evidence that rich offline datasets can modernize rule-based G2P for real-time accessibility applications, not just train larger neural models.

Load-bearing premise

The load-bearing premise is that SentenceBench, the evaluation set on which every headline homograph number is computed, is a valid independent gold standard even though it was created by the same group with the same LLM phonemization pipeline that labeled the training data—and that GPT-4o's phonemization (6.43% PER, 64% homograph accuracy) is accurate enough to learn from; if either premise gives way, the reported ~30-point gains are inflated.

Editorial extensions

If this is right

  • If the reported numbers hold, a rule-based G2P engine can give up almost none of its speed while gaining roughly 30 points of homograph accuracy, making screen-reader quality in Persian materially better without new hardware.
  • The HomoRich dataset gives Persian TTS and accessibility research a public, license-free resource, lowering the barrier for reproducing or extending the results.
  • The same three-phase fine-tuning recipe—general G2P, LLM homograph sentences, human homograph sentences—should transfer to other low-resource languages that can assemble a balanced homograph corpus.
  • The statistical context-overlap module can be dropped into other fast rule-based G2P systems, not only eSpeak, whenever an annotated homograph corpus exists.
  • Homo-T5's competitive results from a dataset an order of magnitude smaller than the synthetic GE2PE corpus suggest the bottleneck is homograph balance and context diversity, not raw data volume.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: because both the training labels and the SentenceBench test set come from the same LLM phonemization pipeline, the reported gain could partly measure how well the models reproduce that pipeline's biases; an independently human-annotated test set is the natural check.
  • Editorial inference: the context-overlap approach is a modern restatement of classic decision-list homograph disambiguation, and its success here suggests that older statistical methods deserve revisiting in low-resource languages once balanced data exist.
  • Editorial inference: a stronger test of the central thesis would be to swap the LLM-generated sentences out of HomoRich and retrain; if homograph accuracy falls sharply, the recipe's value lies in LLM data generation, and if it holds, human sentences carry most of the signal.
  • Editorial inference: the method's restriction to 285 curated homographs means it will not handle rare or unseen homographs; extending the pipeline to open-vocabulary homograph discovery would be the next step.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper addresses Persian grapheme-to-phoneme (G2P) homograph disambiguation. It introduces HomoRich, a 528,891-sentence Persian dataset built from human-written and GPT-4o-generated sentences plus CommonVoice, ManaTTS, and GPTInformal, with LLM-based phoneme labels, and it proposes three data-augmentation methods. The authors fine-tune the existing GE2PE model into Homo-GE2PE and augment the rule-based eSpeak NG engine with a statistical context-overlap disambiguation module to create HomoFast eSpeak. On the authors' SentenceBench benchmark (400 sentences), Homo-GE2PE raises homograph accuracy from 47.17% to 76.89% and HomoFast eSpeak from 43.87% to 74.53%, while reducing PER; Homo-T5, trained only on HomoRich, reaches 76.32% homograph accuracy. The paper argues that rich offline datasets can upgrade both neural and rule-based G2P systems for latency-sensitive accessibility applications.

Significance. If the reported results are correct, the paper is a valuable contribution to low-resource G2P and accessibility: it releases a permissively licensed dataset, a fine-tuned neural model, and an enhanced open-source rule-based engine, and it demonstrates that a simple statistical module can give eSpeak competitive homograph disambiguation while retaining low latency. Multi-run reporting with standard deviations and public code/scripts are strengths. However, the headline numbers rest entirely on SentenceBench, a self-authored 400-sentence benchmark whose construction is not documented; combined with the disclosed 64% homograph accuracy and 6.43% PER of the GPT-4o labeling pipeline, the independence and gold-standard quality of the evaluation are not established. The exact weighted-overlap formula for HomoFast eSpeak is also unspecified, impairing reproducibility. The claims are plausible and worth pursuing, but they require an independent or fully documented evaluation before they can be accepted at face value.

major comments (3)
  1. [§4, Table 3; Appendix H] The central claim of roughly 30-point homograph-accuracy gains is evaluated exclusively on SentenceBench, a benchmark created by the same research group in prior work (Qharabagh et al., 2025a; Fetrat, 2024b). The manuscript never documents how SentenceBench was annotated, whether native-speaker adjudication was used, or whether its labels are independent of the GPT-4o phonemization pipeline used to label HomoRich. Appendix H explicitly states that no specific validation was performed on the LLM-generated or human-provided data. Because the training data and the evaluation data could share the same systematic label errors, the reported +29.72 and +30.66 point gains may reflect agreement with a biased labeler rather than correct disambiguation. Please evaluate on an external, native-speaker-adjudicated test set, or at minimum document SentenceBench's annotation process and demonstrate that its labels were not produced or influenced by the same LLM pipeline.
  2. [§3.2.2] The proposed HomoFast eSpeak method is described as computing a 'weighted overlap' between the context words of a new sentence and each pronunciation's context list, with normalization by list length, but no equation, no definition of the weights, no context-window size, no stopword list, and no pseudocode are provided. This is the core mechanism behind the paper's second headline result (HomoFast eSpeak at 74.53% homograph accuracy), and the method is not reproducible as written. Please specify the full algorithm, including how the context lists are constructed and how the overlap scores are weighted and normalized.
  3. [§3.1; Appendix H] Dataset quality is a load-bearing premise: HomoRich is generated with an LLM pipeline whose reported homograph accuracy is only 64% and PER is 6.43%, and the datasheet states that no validation was performed on the LLM-generated or human-provided data beyond the collection methods described. Given that Homo-GE2PE and Homo-T5 are trained on these labels and evaluated on a same-group benchmark, the paper needs at least a manual error analysis of a random sample of HomoRich, with per-source error rates, to support the assertion that the data are 'high-quality.' Without such analysis, the contribution of data quality versus label noise or benchmark overlap cannot be separated.
minor comments (6)
  1. [Abstract; Conclusion] The text describes the gains as 'approximate 30% improvement' and '30.66% improvement in homograph disambiguation,' but the reported changes are percentage-point increases (e.g., GE2PE from 47.17% to 76.89% is +29.72 points, which is a 63.0% relative improvement). Please correct the wording to 'percentage points' or state the relative improvement explicitly.
  2. [§2; References] Many citations appear as bare years in the text (e.g., '(2020)', '(2025a)', '(2024)') and are not resolved to proper author-year entries in the reference list. The manuscript needs a complete and consistent citation cleanup.
  3. [§4; Figure 9] The inference-time comparison between HomoFast eSpeak and eSpeak is not apples-to-apples because Section 4 states that HomoFast eSpeak benefits from 'a newly added feature that enables processing of larger text segments in a single run.' Please report per-sentence or otherwise controlled segmentation speed so the comparison reflects algorithmic efficiency rather than an implementation artifact.
  4. [§3.1.2] The three augmentation methods are described only qualitatively, with no examples or quality checks. Synonym replacement and sentence reordering can produce unnatural or ungrammatical sentences, and the claim that reordering does not affect pronunciation (except Ezafe) should be validated on a sample. Please provide examples and a small human or automatic evaluation of augmented data quality.
  5. [§4; Table 3] SentenceBench contains only 400 sentences, but the paper does not report the number of homograph instances or per-homograph coverage. For a test set this small, reporting confidence intervals or a per-homograph breakdown would strengthen the claim that the 30-point gains are not driven by a few easy items.
  6. [Appendix E] The datasheet notes that the approximately 200 human participants were not compensated and that no consent-revocation mechanism was provided. For a released dataset, this should be discussed more explicitly, including whether ethical review is needed or was waived.

Circularity Check

1 steps flagged · score 4.0 of 10

Core ~30-point homograph gains rest on the authors' own SentenceBench and the same prior-work LLM pipeline; independent baselines keep the claim from being fully circular.

  1. self citation load bearing [Section 4, first paragraph (Results); also Section 3.1 and Appendix H]
    "there was no public sentence-level dataset suitable for benchmarking homograph accuracy of G2P systems prior to our LLM-Powered G2P work (2025a), which introduced SentenceBench (Fetrat, 2024b). We also adopted this dataset as the primary benchmark in our experiments."

    All headline homograph numbers (Homo-GE2PE 76.89% vs GE2PE 47.17%; HomoFast eSpeak 74.53% vs eSpeak 43.87%) are computed on SentenceBench, a benchmark created by the same authors in prior work. The same prior work also supplies the phonemization recipe used to label HomoRich: the paper says 'We use the most effective method from that study to phonemize our corpus,' and Appendix H states 'No specific validation was performed on the LLM-generated or human-provided data beyond the collection methods described in the paper' and that phonemization has 64% homograph accuracy.

full rationale

Most of the paper's machinery is not circular: HomoRich is an original corpus assembled from KaamelDict, ManaTTS, GPTInformal, CommonVoice, human-written sentences, and GPT-4o generations; Homo-GE2PE is a genuine fine-tune of the external GE2PE model; and HomoFast eSpeak is a statistical context-overlap module inserted into the external eSpeak engine. Improvements are computed against independently developed baselines (GE2PE and eSpeak) on the same benchmark, so the gains are not analytically forced by the training objective. However, the evaluation set is the authors' own SentenceBench from prior work, and the same prior work provides the LLM phonemization method used to label HomoRich. Since the paper neither documents SentenceBench's annotation protocol nor states that it was kept independent of the GPT-4o pipeline, the benchmark cannot rule out the possibility that the reported ~30-point gains reflect agreement with the same LLM labeler rather than with ground truth. This is a load-bearing self-citation, but not a full reduction: the context lists and fine-tuned model weights are not defined in terms of SentenceBench, and the baselines are external systems.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central new artifact is a dataset and two system variants, not a new physical or theoretical entity. The main unpaid assumptions are label quality and benchmark independence; the main unstated design choice is the exact statistical scoring formula behind the 'weighted overlap'.

free parameters (2)
  • Context-list similarity weights in HomoFast eSpeak = not disclosed
    Section 3.2.2 describes a 'weighted overlap' between context words and per-pronunciation context lists but gives no formula; any weights, or the decision to use unweighted overlap, are modeling choices that determine the 74.53% result.
  • Fine-tuning hyperparameters for T5 and GE2PE = lr=5e-4, batch size 32, epochs 5/20/50
    These hyperparameters were chosen by the authors and are not derived from the problem; they affect the three-phase fine-tuning results in Section 3.2.1.
assumptions (3)
  • domain assumption GPT-4o phonemization is accurate enough to serve as training labels for G2P.
    Section 3.1 states the LLM method has PER 6.43% and homograph accuracy 64%; the paper assumes this noise does not overturn the measured gains.
  • domain assumption SentenceBench is a reliable, independent test set for homograph disambiguation.
    All headline numbers in Table 3 use SentenceBench from the authors' prior work; the paper does not document its annotation process or demonstrate that it is free of the LLM labeling approach used for HomoRich.
  • domain assumption POS-tagger-based Ezafe reconstruction correctly maps phoneme representations.
    Appendix B uses a POS tagger with 99.249% accuracy to insert Ezafe markers; errors in this step add label noise to the GE2PE-compatible phoneme format.

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Cite this review

Pith. "Pith review of Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models." pith.science (2026). https://pith.science/paper/TWKVZ3SQ

@misc{pith2026250512973,
  author       = {Pith},
  title        = {Pith review of: Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TWKVZ3SQ}},
  note         = {Machine review of arXiv:2505.12973}
}
read the original abstract

Homograph disambiguation remains a significant challenge in grapheme-to-phoneme (G2P) conversion, especially for low-resource languages. This challenge is twofold: (1) creating balanced and comprehensive homograph datasets is labor-intensive and costly, and (2) specific disambiguation strategies introduce additional latency, making them unsuitable for real-time applications such as screen readers and other accessibility tools. In this paper, we address both issues. First, we propose a semi-automated pipeline for constructing homograph-focused datasets, introduce the HomoRich dataset generated through this pipeline, and demonstrate its effectiveness by applying it to enhance a state-of-the-art deep learning-based G2P system for Persian. Second, we advocate for a paradigm shift - utilizing rich offline datasets to inform the development of fast, rule-based methods suitable for latency-sensitive accessibility applications like screen readers. To this end, we improve one of the most well-known rule-based G2P systems, eSpeak, into a fast homograph-aware version, HomoFast eSpeak. Our results show an approximate 30% improvement in homograph disambiguation accuracy for the deep learning-based and eSpeak systems.

Figures

Figures reproduced from arXiv: 2505.12973 by the authors.

Figure 1
Figure 1. Prompt for generating homograph sentences. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. LLM-powered G2P workflow (2025a) [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Dataset structure with example entry [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Data source distribution in HomoRich dataset. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 6
Figure 6. Figure 6: Illustration of our three data augmentation methods for homograph disambiguation. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Learning curves across fine-tuning phases. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Overview of the proposed statistical homograph disambiguation approach. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: Inference speed and phoneme error rate (PER) of available and proposed G2P tools. [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: Comparison of two commonly used phoneme representations for Persian sounds. [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 11
Figure 11. Figure 11: Distribution of sentence word counts. (a) Phase 1 (5 epochs) (b) Phase 2 (20 epochs) (c) Phase 3 (50 epochs) [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]
Figure 12
Figure 12. Figure 12: Learning curves across fine-tuning phases of T5. [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: Phoneme Error Rate (PER) of previous and proposed G2P tools/models with error bars indicating [PITH_FULL_IMAGE:figures/full_fig_p019_13.png]
Figure 14
Figure 14. Figure 14: Homograph Accuracy of previous and proposed G2P tools/models with error bars indicating standard [PITH_FULL_IMAGE:figures/full_fig_p020_14.png]
Figure 15
Figure 15. Figure 15: Inference Time (s) of previous and proposed G2P tools/models plotted on a logarithmic scale with error [PITH_FULL_IMAGE:figures/full_fig_p021_15.png]

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