Optimizing Estonian TV Subtitles with Semi-supervised Learning and LLMs

Artem Fedorchenko; Tanel Alum\"ae

REVIEW 5 major objections 4 minor 26 references

Optimizing Estonian TV Subtitles with Semi-supervised Learning and LLMs

T0 review · 5 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Fine-tuning Whisper on human Estonian subtitles, then retraining on two rounds of model-generated pseudo-labels, improves subtitle quality across all metrics; test-time LLM editing adds further gains.

desk verdict Useful engineering result for Estonian subtitles, but the paper overclaims pseudo-labeling gains across all metrics when its own Table 2 shows t-BLEURT dropping. read the letter →

arxiv 2501.05234 v1 pith:LYI47JO4 submitted 2025-01-09 cs.CL cs.AIcs.LGeess.AS

Artem Fedorchenko , Tanel Alumäe This is my paper · ORCID

classification cs.CLcs.AIcs.LGeess.AS

keywords EstoniansubtitlingWhispersemi-supervisedlearningpseudo-labelingLLMpost-editingSubERBLEURT

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

This paper tries to show that a practical recipe can generate near-human-quality Estonian TV subtitles from audio alone. The recipe is: fine-tune the Whisper speech model on human-created subtitles, retrain it twice on its own pseudo-labels for nearly 4,000 hours of unlabeled broadcast audio, and post-edit the output with GPT-4o at inference time. The authors report that each ingredient that works is measurable: pseudo-labeling improves every evaluation metric, and test-time LLM editing helps further, while LLM editing during training does not. If true, this gives broadcasters and streaming services a way to produce same-language subtitles for low-resource languages without collecting new human transcripts.

What carries the argument

The load-bearing mechanism is iterative pseudo-labeling: the current model transcribes unlabeled audio, those transcriptions are added to the supervised training set, and the model is retrained for two rounds. Training uses speed perturbation, SpecAugment, and a weighted loss $L_{\text{total}} = (1-\lambda)L_{\text{supervised}} + \lambda L_{\text{pseudo}}$ with $\lambda = 0.35$ chosen by Optuna. Quality is measured with SubER, an edit-distance metric that accounts for subtitle timing and segmentation shifts, plus two BLEURT-based semantic similarity variants (t-BLEURT and AS-BLEURT). LLM post-editing operates on chunks of 40 subtitle blocks, with a verification step that re-requests the LLM when block numbers or timestamps are corrupted.

What would settle it

Take a held-out subset of the 3,923-hour unlabeled set, transcribe it with the baseline model, and compare the pseudo-labels against human subtitles for the same broadcasts; then train a control model on an equally sized randomly sampled subset of that unlabeled audio without pseudo-labels—if the pseudo-label-trained model does not beat this control on SubER, the iterative gain claim collapses.

Watch

Extended reading notes

Core claim

The central discovery is that iterative pseudo-labeling of a large unlabeled dataset improves subtitle quality across all tested metrics, and that test-time LLM post-editing adds a further gain, in the specific setting of Estonian TV subtitles. Starting from a Whisper large-v3 model fine-tuned on 778 hours of human subtitles (SubER 35.1), two training iterations on model-generated pseudo-labels from 3,923 hours of unlabeled audio lower SubER to 33.4 and raise AS-BLEURT from 0.799 to 0.853. Applying GPT-4o post-editing at test time improves the best combined system to SubER 33.1 and AS-BLEURT 0.858, whereas applying GPT-4o to pseudo-labels during training gives no further gain. The authors also show that fine-tuning on subtitles rather than verbatim transcripts matters greatly, cutting SubER from 51.5 to 35.1.

Load-bearing premise

The model's own transcriptions of thousands of hours of unlabeled Estonian broadcasts are accurate enough to serve as training targets, and retraining on them without any confidence filtering will not reinforce systematic errors.

Editorial extensions

If this is right

The full pipeline—supervised fine-tuning, two rounds of pseudo-labeling, and test-time GPT-4o editing—yields the best subtitle quality, with SubER 33.1 and AS-BLEURT 0.858.
Subtitle-specific fine-tuning matters: switching from verbatim transcripts to human subtitles cuts SubER from 51.5 to 35.1.
Pseudo-labeling gains accumulate over iterations: iteration 1 gives SubER 34.5, and iteration 2 gives 33.4 without LLM editing.
LLM post-editing is only useful at inference: editing pseudo-labels during training does not improve the final model.

Reading between the lines

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

The SubER gains from pseudo-labeling may partly reflect simply training on more in-domain audio; a control experiment that trains on an equal number of hours of unlabeled audio without pseudo-labels would separate the two effects.
The recipe should transfer to other languages that have professionally subtitled broadcast corpora but limited verbatim transcripts, since it uses only unlabeled audio plus existing subtitles.
Real-time subtitling would require replacing GPT-4o with a faster distilled or on-device editor; the paper's chunked 40-block post-editing is designed for parallelization and could feed a streaming pipeline.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a solid, practical paper about building a same-language subtitle system for Estonian TV, and the negative result about LLM post-editing during training is genuinely useful. But the claim in the introduction and §4.6 that iterative pseudo-labeling improves 'across all metrics' is directly contradicted by Table 2: going from the supervised subtitle model (A) to the two-iteration pseudo-labeling model without LLM post-editing, SubER improves (35.1→33.4) and AS-BLEURT improves (.799→.853), but t-BLEURT drops (.545→.529). The same pattern already appears at iteration 1 (.545→.526). So the paper's own central statement is false for one of its three metrics. The Wilcoxon test in §4.6 only compares A, B, C (the LLM post-edited variants), not the raw pseudo-labeling comparison, so it doesn't fix this.

What's actually new: a concrete recipe for Estonian subtitles—fine-tune Whisper large-v3 on 778h of human subtitles, do two rounds of pseudo-labeling on 3,923h of unlabeled broadcast audio, and post-edit with GPT-4o at test time. That gets SubER from 35.1 to 33.1 and t-BLEURT from .545 to .598. The observation that using GPT-4o to clean pseudo-labels during training does not help, contrary to Xi et al. (2024), is a useful negative result. They also release the fine-tuned model and a demo, which is more than most papers do.

Soft spots besides the overclaim: the test set is 17 recordings (about 1.7% of the supervised set), there are no error bars or per-recording breakdowns, and there is no human evaluation despite the abstract saying the approach 'holds promise for creating subtitle quality close to human standard'—that's speculative. The pseudo-labeling step is confounded with simply training on more in-domain audio; there is no control that adds a comparable amount of supervised audio or selects subsets of pseudo-labels based on confidence. The reader's weakest_assumption is right: without measuring pseudo-label accuracy on the unlabeled set, we can't know whether the gain comes from self-training or from more data. Also, only GPT-4o helped at test time; all the open-source LLMs made SubER worse, so the result is tied to a paid API.

The citation pattern looks fine—related work is credited, including Xi et al. and the earlier pseudo-labeling ASR papers.

Bottom line: this is a useful engineering paper for a low-resource language, with a clear and honest (if overclaimed) empirical setup. A serious referee should engage with it, but the authors need to correct the 'across all metrics' claim, add error bars or a larger test set, and ideally run a human evaluation or at least an error analysis before publication. I'd cite it if I worked on subtitling or semi-supervised ASR.

Referee Report

5 major / 4 minor

Summary. The paper describes a system for generating same-language Estonian TV subtitles by fine-tuning Whisper large-v3 on human subtitles, then applying two iterations of pseudo-labeling on 3,923 hours of unlabeled broadcast audio, optionally with GPT-4o-based post-editing of pseudo-labels at training time and/or of test hypotheses at inference. Experiments report SubER, t-BLEURT, and AS-BLEURT on a 17-recording test set, with Wilcoxon signed-rank tests comparing three highlighted models. The authors claim that iterative pseudo-labeling improves subtitle quality across all metrics and that test-time LLM post-editing further helps, while training-time LLM post-editing does not.

Significance. If the empirical findings were fully supported, the paper would offer a practical and reproducible recipe for under-resourced language subtitling: fine-tune Whisper on a modest set of human subtitles, retrain on model-generated pseudo-labels, and post-edit with a strong LLM at inference. The authors ship the fine-tuned model and a demo, which is a concrete contribution to the subtitling and ASR community. However, the central claim is overstated relative to the paper's own data (t-BLEURT decreases with pseudo-labeling alone), the statistical tests do not isolate the pseudo-labeling effect, and several methodological gaps (test-set reuse, absent pseudo-label quality checks, no human evaluation) prevent the paper from being accepted as is. The core recipe may still be valid, but the manuscript needs substantial revision to align claims with evidence.

major comments (5)

[§1, §4.6, Table 2] The paper claims in the introduction and in §4.6 that iterative pseudo-labeling improves subtitle quality 'across all metrics'. This is contradicted by Table 2: the model with two iterations of pseudo-labeling and no LLM (SubER 33.4, t-BLEURT .529, AS-BLEURT .853) has lower t-BLEURT than the supervised baseline model A (SubER 35.1, t-BLEURT .545, AS-BLEURT .799). The same holds for iteration 1 (t-BLEURT .526). Since t-BLEURT is one of the paper's three primary metrics, the statement as written is false. The authors should either revise the claim to name only SubER and AS-BLEURT, or provide a substantive argument why the t-BLEURT drop is irrelevant to the conclusion.
[§4.6] The Wilcoxon signed-rank tests are computed only between models A, B, and C. Model B is the baseline plus test-time LLM, and model C adds pseudo-labeling on top of that, so these tests do not isolate the effect of pseudo-labeling. They cannot support the claim that pseudo-labeling alone improves metrics. Pairwise tests between model A and the pseudo-label-only models (e.g., the iteration-2 row without LLM) should be reported, or the claim should be explicitly restricted to the full pipeline.
[§4.4, §4.6] The pseudo-labeling setup has no confidence filtering and no report of pseudo-label quality on the unlabeled set. Because the unlabeled set is roughly five times larger than the supervised set and contains news programs not present in the supervised data, the observed SubER and AS-BLEURT gains could be due to simply adding more in-domain audio rather than to the iterative refinement mechanism. The authors should measure pseudo-label accuracy (e.g., by running the pseudo-labeling model on a held-out subset of human subtitles and computing WER/SubER) or compare against a control that trains on the same amount of audio with a fixed teacher. At the very least, this confound should be discussed explicitly.
[§4.5, Table 1] The selection of GPT-4o as the post-editing LLM is based on SubER scores computed on the test set ('Table 1 shows the SubER results on test data'). The same test set is then used to report final results, making the reported test-time LLM gains optimistic. The authors should reserve a development set for such model-selection decisions or otherwise qualify the reported gains. Similarly, the Optuna-based choice of λ in §4.4 should state which data split was used for optimization.
[§4.6, Abstract] The abstract's statement that the approach 'holds promise for creating subtitle quality close to human standard' and the §4.6 assertion that 'minimal manual post-editing would be required to achieve error-free subtitles' are based only on the authors' subjective assessment; no human evaluation was conducted. This is not a minor omission but a missing piece of evidence for a quality claim directed at human viewers. A small-scale human rating study or an explicit removal of this claim from the abstract and discussion is needed.

minor comments (4)

[Table 2] The meaning of the '!' symbol in the two LLM-post-editing columns is not defined in the table caption. Also, the column headers 'Pseudo-label LLM-post-editing?' and 'Test-time LLM-post-editing?' are easy to confuse; consider renaming them 'LLM post-editing of pseudo-labels (training time)' and 'LLM post-editing of test hypotheses'.
[§4.6] Report p-values as '<0.001' rather than '0.000', and specify whether the Wilcoxon test was one-sided or two-sided and that it was computed over 17 test recordings.
[§1] There is a typo in the introduction: 'it’s use at training time' should be 'its use at training time'.
[§4.2] The phrase 'BLEURT outputs scores that usually in the range of 0..1' is awkward and vague; rephrase to state the actual score range and that higher is better.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: all key claims are evaluated against held-out human references with standard external metrics.

full rationale

The paper is an empirical study, not a derivation, and its central claims are not equivalent to its inputs by construction. The training pipeline uses human-generated Estonian subtitles as supervised data, model-generated pseudo-labels from a large unlabeled set, and LLM post-editing; the reported improvements are measured with held-out human reference subtitles using external metrics (SubER, t-BLEURT, AS-BLEURT). Because the reference subtitles are human-authored and independent of the model's own outputs, the pseudo-labeling results are not forced: the model could have failed to improve or could have drifted toward its own errors, and the paper's own Table 2 shows mixed results across metrics. The self-citation to Alumäe et al. (2023) is used only to provide a verbatim-transcript baseline model for comparison, not to justify the target result or to forbid alternatives. No uniqueness theorem, no ansatz smuggled in via citation, and no renaming of a known result are present. The abstract's wording that iterative pseudo-labeling 'improves subtitle quality across all metrics' is internally inconsistent with Table 2, where the two-iteration pseudo-labeling model without LLM post-editing shows t-BLEURT dropping from .545 to .529 while SubER improves from 35.1 to 33.4; however, this is a correctness or consistency concern, not circularity. Accordingly, the circularity score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The system relies on known ASR and NLP components (Whisper, GPT-4o, BLEURT, SubER) and standard semi-supervised training. The only hand-tuned quantities are the pseudo-label loss weight, the LLM failure threshold, and the chunk size. No new entities, forces, or mathematical objects are introduced.

free parameters (4)

lambda (pseudo-label loss weight) = 0.35
Chosen empirically via Optuna in §4.4; controls the balance between supervised and pseudo-labeled loss.
reference check failure threshold = 3
Set by hand in §4.5; if the LLM fails timestamp/block verification more than 3 times, the original subtitle is kept.
LLM chunk size = 40 subtitle blocks
Chosen in §4.5 for parallelization and token limits; not systematically optimized.
number of pseudo-label iterations = 2
Two iterations were run; no criterion is given for stopping after two.

assumptions (4)

domain assumption SubER and BLEURT scores reflect subtitle quality as judged by humans.
The paper uses these as primary metrics (§4.2) and notes BLEURT is a learned metric correlated with human judgments, but no human evaluation is performed in this work.
domain assumption Pseudo-labels from the current Whisper model are accurate enough to serve as training targets.
Iterative pseudo-labeling in §4.4 trains on the model's own outputs without confidence filtering; if many pseudo-labels contain systematic errors, self-training could amplify them.
domain assumption Human-generated subtitles are a valid gold standard for the subtitle generation task.
The supervised set uses subtitles produced by expert subtitlers for deaf and hard-of-hearing viewers (§4.1), and these are used as fine-tuning targets and references.
standard math Wilcoxon signed-rank test assumptions hold for the paired metric differences.
Used in §4.6 to claim significance between models A, B, and C; assumes paired observations and ordinal differences.

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Cite this review

Pith. "Pith review of Optimizing Estonian TV Subtitles with Semi-supervised Learning and LLMs." pith.science (2026). https://pith.science/paper/LYI47JO4

@misc{pith2026250105234,
  author       = {Pith},
  title        = {Pith review of: Optimizing Estonian TV Subtitles with Semi-supervised Learning and LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LYI47JO4}},
  note         = {Machine review of arXiv:2501.05234}
}

read the original abstract

This paper presents an approach for generating high-quality, same-language subtitles for Estonian TV content. We fine-tune the Whisper model on human-generated Estonian subtitles and enhance it with iterative pseudo-labeling and large language model (LLM) based post-editing. Our experiments demonstrate notable subtitle quality improvement through pseudo-labeling with an unlabeled dataset. We find that applying LLM-based editing at test time enhances subtitle accuracy, while its use during training does not yield further gains. This approach holds promise for creating subtitle quality close to human standard and could be extended to real-time applications.

Figures

Figures reproduced from arXiv: 2501.05234 by the authors.

**Figure 1.** Pseudo-labels generated by model are either passed through LLM or used as as is. — a semi-supervised learning technique that enables model refinement on unlabeled data. Starting with an initial model trained on supervised data, we generate pseudo-labels for unlabeled samples and use these to retrain the model iteratively. Our approach, which we illustrate in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

26 extracted references · 14 canonical work pages

[1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

a e, Joonas Kalda, K \

Tanel Alum \"a e, Joonas Kalda, K \"u lliki Bode, and Martin Kaitsa. Automatic closed captioning for E stonian live broadcasts. In Proc. NoDaLiDa, T \'o rshavn, Faroe Islands, May 2023. URL: https://aclanthology.org/2023.nodalida-1.49

work page 2023
[4]

Data augmentation and teacher-student training for LF-MMI based robust speech recognition

Asadullah and Tanel Alum \"a e. Data augmentation and teacher-student training for LF-MMI based robust speech recognition. In Proc. TSD, pages 403--410. Springer, 2018

work page 2018
[5]

Subtitle translation as markup translation

Colin Cherry, Naveen Arivazhagan, Dirk Padfield, and Maxim Krikun. Subtitle translation as markup translation. In Proc. Interspeech, pages 2237--2241, 2021

work page 2021
[6]

Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter

Yu Xi, Wen Ding, Kai Yu, and Junjie Lai. Semi-supervised learning for code-switching ASR with large language model filter. arXiv preprint arXiv:2407.04219, 2024

work page Pith review arXiv 2024
[7]

Averaging weights leads to wider optima and better generalization

Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. Averaging weights leads to wider optima and better generalization. In Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), pages 876--885. AUAI Press, 2018

work page 2018
[8]

Comparing the impact of professional and automatic closed captions on video-watching experience

Hyunju Kim, Yan Tao, Chuanrui Liu, Yuzhuo Zhang, and Yuxin Li. Comparing the impact of professional and automatic closed captions on video-watching experience. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

work page 2023

Show all 26 references

[9]

Decoupled weight decay regularization

Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019

2019
[10]

Can generative large language models perform asr error correction? arXiv preprint arXiv:2307.04172, 2023

Rao Ma, Mengjie Qian, Potsawee Manakul, Mark Gales, and Kate Knill. Can generative large language models perform asr error correction? arXiv preprint arXiv:2307.04172, 2023

2023 arXiv
[11]

Anantha, Daben Liu, and Sashank Gondala

Ernest Pusateri, Anmol Walia, Anirudh Kashi, Bortik Bandyopadhyay, Nadia Hyder, Sayantan Mahinder, R. Anantha, Daben Liu, and Sashank Gondala. Retrieval augmented correction of named entity speech recognition errors. arXiv preprint arXiv:2409.06062, 2024

2024 arXiv
[12]

Multi-stage large language model correction for speech recognition

Jie Pu, Thai-Son Nguyen, and Sebastian St \"u ker. Multi-stage large language model correction for speech recognition. arXiv preprint arXiv:2310.11532, 2023

2023 arXiv
[13]

Evaluating machine translation output with automatic sentence segmentation

Evgeny Matusov, Gregor Leusch, Oliver Bender, and Hermann Ney. Evaluating machine translation output with automatic sentence segmentation. In Proc. IWSLT, 2005

2005
[14]

Optuna: A next-generation hyperparameter optimization framework

Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2019

2019
[15]

Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D

Daniel S. Park, William Chan, Yu Zhang, Chung-Cheng Chiu, Barret Zoph, Ekin D. Cubuk, and Quoc V. Le. SpecAugment: A simple data augmentation method for automatic speech recognition. In Interspeech 2019. ISCA, September 2019. URL: http://dx.doi.org/10.21437/Interspeech.2019-26...

2019 doi
[16]

Survey: Why America is obsessed with subtitles

Nadiia Mykhalevych and Preply. Survey: Why America is obsessed with subtitles. 2024. URL: https://preply.com/en/blog/americas-subtitles-use/

2024
[17]

Robust speech recognition via large-scale supervision

Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. Robust speech recognition via large-scale supervision. arXiv preprint arXiv:2212.04356, 2022

2022 arXiv
[18]

Thibault Sellam, Dipanjan Das, and Ankur P. Parikh. BLEURT : Learning robust metrics for text generation. In Proc. ACL, 2020

2020
[19]

Silero VAD : Pre-trained enterprise-grade voice activity detector

Silero Team. Silero VAD : Pre-trained enterprise-grade voice activity detector. 2021. URL: https://github.com/snakers4/silero-vad

2021
[20]

SubER : A metric for automatic evaluation of subtitle quality

Patrick Wilken, Panayota Georgakopoulou, and Evgeny Matusov. SubER : A metric for automatic evaluation of subtitle quality. arXiv preprint arXiv:2205.05805, 2022

2022 arXiv
[21]

Iterative pseudo-labeling for speech recognition

Qiantong Xu, Tatiana Likhomanenko, Jacob Kahn, Awni Hannun, Gabriel Synnaeve, and Ronan Collobert. Iterative pseudo-labeling for speech recognition. arXiv preprint arXiv:2005.09267, 2020

2005 arXiv
[22]

Semi-supervised training of deep neural networks

Karel Vesel \`y , Mirko Hannemann, and Luk \'a s Burget. Semi-supervised training of deep neural networks. In 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pages 267--272. IEEE, 2013

2013
[23]

Parikh, Sebastian Gehrmann, and Thibault Sellam

Amy Pu, Hyung Won Chung, Ankur P. Parikh, Sebastian Gehrmann, and Thibault Sellam. Learning compact metrics for MT . In Proc. EMNLP, 2021

2021
[24]

Using untranscribed training data to improve performance

George Zavaliagkos, Man-Hung Siu, Thomas Colthurst, and Jayadev Billa. Using untranscribed training data to improve performance. In Proc. ICSLP, volume 1998, 1998

1998
[25]

Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clément Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mari...

1910 arXiv
[26]

Individual comparisons by ranking methods

Frank Wilcoxon. Individual comparisons by ranking methods. Biometrics Bulletin, 1(6):80--83, 1945. URL: https://doi.org/10.2307/3001968, DOI: 10.2307/3001968

1945 doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

URL: " 'urlintro :=

ENTRY address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year eprint doi pubmed url lastchecked label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block STRINGS urlintro eprinturl eprintpr...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

a e, Joonas Kalda, K \

Tanel Alum \"a e, Joonas Kalda, K \"u lliki Bode, and Martin Kaitsa. Automatic closed captioning for E stonian live broadcasts. In Proc. NoDaLiDa, T \'o rshavn, Faroe Islands, May 2023. URL: https://aclanthology.org/2023.nodalida-1.49

work page 2023

[4] [4]

Data augmentation and teacher-student training for LF-MMI based robust speech recognition

Asadullah and Tanel Alum \"a e. Data augmentation and teacher-student training for LF-MMI based robust speech recognition. In Proc. TSD, pages 403--410. Springer, 2018

work page 2018

[5] [5]

Subtitle translation as markup translation

Colin Cherry, Naveen Arivazhagan, Dirk Padfield, and Maxim Krikun. Subtitle translation as markup translation. In Proc. Interspeech, pages 2237--2241, 2021

work page 2021

[6] [6]

Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter

Yu Xi, Wen Ding, Kai Yu, and Junjie Lai. Semi-supervised learning for code-switching ASR with large language model filter. arXiv preprint arXiv:2407.04219, 2024

work page Pith review arXiv 2024

[7] [7]

Averaging weights leads to wider optima and better generalization

Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. Averaging weights leads to wider optima and better generalization. In Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence (UAI), pages 876--885. AUAI Press, 2018

work page 2018

[8] [8]

Comparing the impact of professional and automatic closed captions on video-watching experience

Hyunju Kim, Yan Tao, Chuanrui Liu, Yuzhuo Zhang, and Yuxin Li. Comparing the impact of professional and automatic closed captions on video-watching experience. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

work page 2023