Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations

Aarne Talman; Antti Suni; Hande Celikkanat; J\"org Tiedemann; Martti Vainio; Sofoklis Kakouros

REVIEW 3 major objections 5 minor 36 references

Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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

Pith's one-line read Pre-trained BERT representations predict prosodic prominence from text at 83.2% accuracy.

desk verdict A valuable large public prosody-prominence dataset with honest baselines, but the headline BERT advantage lacks significance testing and the auto-generated labels need human validation on the new corpus. read the letter →

arxiv 1908.02262 v1 pith:4OPFZE4R submitted 2019-08-06 cs.CL

Aarne Talman , Antti Suni , Hande Celikkanat , Sofoklis Kakouros , Jörg Tiedemann , Martti Vainio This is my paper · ORCID

classification cs.CL

keywords prosodicprominencepitchaccentpredictionBERTcontextualizedwordrepresentationssequencelabelingautomaticprosodyannotationtext-to-speechLibriTTS

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 introduces a new benchmark for predicting prosodic prominence — the emphasis a speaker places on a word — from written text alone. The dataset, drawn from the LibriTTS audiobook corpus, contains automatically generated prominence labels for more than 2.8 million words, which the authors say makes it the largest publicly available dataset with prosodic labels. Using this dataset, the paper trains feature-based classifiers, CRFs, BiLSTMs, and fine-tuned BERT, and reports that BERT achieves the highest accuracy in both binary (83.2%) and three-way (68.6%) classification. The authors interpret this as evidence that pre-trained contextualized word representations carry semantic and syntactic knowledge useful for predicting prominence, since BERT outperforms all other models even when trained on only 10% of the data. If correct, this makes large-scale, automatically annotated prosody resources practical and points to pre-trained language models as the default starting point for text-based prosody prediction.

What carries the argument

The load-bearing components are a large automatically annotated dataset and the pre-trained language model used as a sequence labeler. Prominence labels are produced by the Wavelet Prosody Analyzer, which combines pitch, energy, and duration into a composite signal and applies a continuous wavelet transform so that a word is marked prominent if it stands out from its environment across multiple time scales; the continuous values are then discretized into two or three classes, with weights and thresholds tuned on the Boston University corpus. For prediction, the paper takes the last hidden layer of BERT-base as a per-word representation and trains a single fully connected layer on top, fine-tuning BERT for two epochs. The mechanism that carries the argument is the transfer of implicit syntactic and semantic knowledge from BERT's pre-training, which the learning-curve experiments isolate by showing that BERT outperforms all baselines at 5–10% of the training data.

What would settle it

Manually collect perceived-prominence annotations from several listeners on a random subset of the LibriTTS test set and compare each model's predictions to the human labels and to the automatic labels. If BERT's advantage over the BiLSTM largely disappears when scored against human labels, the paper's claim that pre-trained representations help predict perceived prominence would be contradicted.

Watch

Extended reading notes

Core claim

The paper's central claim is that pre-trained contextualized word representations from BERT outperform all tested alternatives on text-based prosodic prominence prediction. Fine-tuning BERT with a single classifier layer on top of its last hidden layer yields 83.2% accuracy in a two-way prominent/non-prominent task and 68.6% in a three-way task, compared with 82.1%/66.4% for a three-layer BiLSTM with GloVe embeddings and 81.8%/66.4% for a CRF. The advantage holds with far less data: at 10% of the training set, BERT reaches 82.6% two-way accuracy, within 0.6 points of full-data performance, while all models have nearly converged at 10%. The authors further show that on the manually annotated Boston University radio news corpus, BERT reaches 82.9% against expert labels, which they read as support for the quality of their automatically generated labels.

Load-bearing premise

The entire benchmark rests on the assumption that the automatically generated acoustic prominence labels are a valid gold standard for perceived prominence; if the labels do not match what listeners hear, the model rankings describe an acoustic heuristic, not human prosody.

Editorial extensions

If this is right

Text-to-speech front-ends should treat pre-trained contextualized word representations as the default input for symbolic prosody prediction, since BERT beats feature-based and recurrent baselines without hand-engineered linguistic features.
For BERT, 10% of the training data yields 2-way accuracy within 0.6 points and 3-way accuracy within 1.7 points of the full-data result, so moderate-sized labeled sets can support meaningful model comparison.
The BERT advantage over BiLSTM grows on a contemporary news test set (3.9 points vs. 1.1 on the audiobook test set), suggesting that pre-training matters more for modern genres than for the pre-1923 source texts.
Automatic acoustic labeling can produce prosody datasets at a scale manual annotation cannot reach, enabling data-hungry neural methods for this task.

Reading between the lines

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

If BERT's edge comes from implicit linguistic knowledge, then domain-adaptive pre-training or adding explicit part-of-speech and syntactic features could push accuracy toward the 80–90% human inter-annotator agreement the paper cites.
The same annotation pipeline can be repurposed for prosodic boundaries, which the paper names as future work; joint prominence-and-boundary models would likely capture structure neither task exposes alone.
Because the auto-labels encode speaker-specific acoustic patterns and the paper trains on single sentences, a speaker-aware or multi-sentence model is a natural extension that might beat the reported BERT numbers.
Using the dataset's real-valued prominence scores for regression, instead of the discretized classes used in the paper, would test how much information the quantization step discards.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

The paper's real contribution is the dataset: 2.8M words of LibriTTS with word-level prosodic prominence labels, released with code and baselines. That is genuinely useful and, as far as I can tell, the largest public resource of its kind. The BERT application is a first, though the recipe is standard; the learning-curve experiments showing BERT catching up with 10% of the data are the most interesting empirical finding. The Boston University corpus cross-check is a good instinct and gives some external anchor.

The soft spots are real but not disqualifying. The headline BERT-vs-BiLSTM gap is +1.1 accuracy points (2-way) and +2.2 (3-way) with no confidence intervals or significance tests. Given the dataset is large, those gaps might be stable, but the paper should show it. The bigger concern is the label construction: the Wavelet Prosody Analyzer is a reasonable acoustic proxy—louder, longer, higher is more prominent—and the weights were tuned on Boston, not on LibriTTS. The paper itself concedes in Section 6 that alignment and signal-processing noise affect test labels and that manual correction would help. So the stress-test worry is fair: the benchmark may rank models on the acoustic annotator rather than on perceived prominence. That said, this is a limitation, not a fatal flaw. The labels are deterministic functions of the speech signal, so the task is well-defined; the open question is how well the auto-labels correspond to human perception. Adding a small manually labeled subset (even a few hundred sentences) would settle it.

The citation pattern looks fine. The annotation method is properly attributed to prior work, the baselines are standard, and the paper does not oversell the dataset's gold-standard status—Section 6 is candid about known noise. No circularity: the labels come from acoustics, not from the text-based models.

Who is this for? People working on prosody prediction, TTS front-ends, and anyone who wants a large sequence-labeling benchmark with a non-lexical target. It deserves a serious referee. My recommendation: accept conditional on (1) significance testing or at least variance estimates, and (2) a small-scale manual validation of the LibriTTS labels. Without those, the central comparison remains under-supported, but the dataset alone justifies publication.

Referee Report

3 major / 5 minor

Summary. The paper introduces a new large-scale dataset and benchmark for word-level prosodic prominence prediction, derived from the LibriTTS corpus and annotated automatically with the Wavelet Prosody Analyzer. The authors describe the annotation pipeline, report dataset statistics, and train several sequence-labeling models (BERT-base, a 3-layer BiLSTM with GloVe embeddings, a CRF, an SVM with GloVe features, and majority-class baselines) on a 2-way and a 3-way prominence classification task. The main empirical claim is that fine-tuned BERT achieves the highest test accuracy (83.2% and 68.6% in the 2-way and 3-way tasks, respectively), outperforming the other models even when trained on only 10% of the data. The paper also reports a cross-corpus evaluation on the Boston University radio news corpus and a small set of qualitative examples.

Significance. Conditional on the validity of the automatic labels, the dataset is a useful community resource: 2.8 million words with prominence labels is an order of magnitude larger than existing resources, and the public release of data and code is a concrete contribution. The Boston cross-corpus result (BERT 82.9% vs. BiLSTM 79.0% on expert labels in Table 4) is a genuinely informative transfer check and gives some evidence that the automatically generated training labels carry signal about perceived prominence. The learning-curve observation that BERT reaches near-full performance with 10% of the training data is an interesting, falsifiable finding. However, the paper's two central claims—that the auto-generated LibriTTS labels constitute a valid benchmark for prosodic prominence and that BERT clearly outperforms a strong BiLSTM baseline—need additional validation before they can be accepted at face value.

major comments (3)

[Section 3, Table 4, Section 6] The LibriTTS gold labels are produced entirely by the Wavelet Prosody Analyzer, with weights and thresholds tuned on the Boston University corpus, and no manual validation is reported on LibriTTS itself. Section 6 concedes that alignment, signal processing, and quantization errors affect the test results and that 'manual correction of a part of the test set could be beneficial.' The Boston experiment in Table 4 is a valuable transfer check, but because the annotation parameters were optimized on Boston and because Boston differs from LibriTTS in genre, recording conditions, and speaking style, it does not establish the quality of the LibriTTS labels. I would like to see a human-agreement study on a random LibriTTS sample (e.g., agreement between the auto labels and expert or naive listeners) and, ideally, model results on that human-annotated subset. Without this, the benchmark may measure prediction of an acoustic proxy rather than perceived prominence.
[Section 4.2, Table 3] The headline claim that BERT demonstrates 'the value of a pre-trained language model in this task' rests on margins of +1.1 accuracy points in the 2-way task and +2.2 points in the 3-way task over the BiLSTM, with no confidence intervals, significance tests, or repeated runs. With 90,063 test tokens these margins may well be statistically significant, but the paper does not establish that, and the practical significance is unclear. I ask for McNemar or bootstrap tests for the BERT-vs-BiLSTM comparisons in Tables 3 and 4, per-seed variance, and effect sizes. This directly affects the abstract and conclusion, which state that BERT outperforms the other models.
[Section 5, Figures 4-7] The claim that 'BERT outperforms the BiLSTM model across all the labels' is supported only by confusion matrices shown as figures, with no per-class precision, recall, or F1 numbers reported in the text. Accuracy alone is insufficient to support this claim, particularly for label 1, where the text says both models have low precision and recall. Quantitative per-class scores are needed, ideally with macro-averaged F1 for the 3-way task, to verify that BERT's advantage is not driven solely by the majority class.

minor comments (5)

[Section 2.1] 'contributes to a large extend' should be 'contributes to a large extent'.
[Section 6] 'with the The Boston University radio news corpus' contains a duplicated article and should be corrected.
[Section 4.1] Hyperparameter details for BERT and BiLSTM are incomplete for reproducibility: no learning rate, optimizer, or random seed is reported; please add these or specify the exact configuration used.
[Figures 2 and 3] Only a single run appears to be plotted per model; adding error bars or shaded intervals from multiple seeds would make the learning-curve comparisons more robust.
[Table 5] The examples would be more informative if each sentence showed the gold label sequence and the predicted label sequence explicitly, rather than relying on visual formatting that is difficult to verify in print.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the text-based prominence prediction benchmark is self-contained, and the auto-labeling method is externally validated.

full rationale

The paper's central claim is that BERT-based models outperform BiLSTM, CRF, SVM, and majority baselines at predicting discrete prominence labels from text. The labels are generated by the Wavelet Prosody Analyzer from acoustic signals, independent of the textual inputs used by the classifiers; no equation in the paper reduces a model output to a fitted label-generation parameter. The annotation weights and thresholds were tuned on the Boston University corpus, not on the LibriTTS test labels, and the paper includes a cross-corpus evaluation against manually annotated Boston expert labels (Table 4), which provides external evidence that the task is not merely self-referential. Self-citation of Suni et al. (2017) for the wavelet annotation method is load-bearing for dataset construction but is not circular: it is a prior published method with an external 85.3% accuracy evaluation, and the present paper's model comparison does not depend on accepting that method's assumptions as ground truth. Section 6 candidly flags the lack of manual correction of the LibriTTS test set as a limitation, but that is a validity/quality concern, not a circularity of the derivation chain. Therefore no prediction reduces to its inputs by construction.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claim depends mainly on the validity of the automatic annotation pipeline and on clean train/test separation. The only fitted numbers that matter for the whole benchmark are the acoustic feature weights and discretization thresholds; the model hyperparameters are standard training choices. No new physical or mathematical entities are introduced.

free parameters (2)

Annotation feature weights (F0, energy, duration) = 1.0, 0.5, 1.0 (multiplicative composition)
Set in the Wavelet Prosody Analyzer to compute the word prominence signal from pitch, energy, and duration; adjusted based on the Boston University radio news corpus (Section 3). These weights determine every label in the dataset, so the benchmark inherits them.
Discretization thresholds for 2-way and 3-way labels = Cut-off for prominent/non-prominent; prominent split into roughly equal halves
Chosen to match Boston corpus performance and to balance classes; the thresholds convert continuous wavelet prominence values into the discrete labels used in all experiments (Section 3, Table 1).

assumptions (4)

domain assumption The wavelet acoustic prominence estimator (louder, longer, higher) is a valid proxy for perceived prosodic prominence.
Section 3; the paper does not validate this on LibriTTS, only indirectly on the Boston corpus (85.3% accuracy) and with the Boston test in Section 4.2.
domain assumption Montreal Forced Aligner word alignments are accurate enough for word-level labeling.
Section 3 uses MFA trained on LibriSpeech; the authors note alignment errors as a noise source in Section 6.
domain assumption The train/dev/test splits of the prosody dataset are speaker-disjoint.
The paper does not confirm this; LibriTTS is speaker-disjoint by design, but the paper should state it for its subset to rule out speaker leakage.
domain assumption Automatic labels are an acceptable gold standard for text-based prominence prediction.
The whole benchmark uses them; the Boston comparison to expert labels is only for BERT and BiLSTM with no significance testing.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations." pith.science (2026). https://pith.science/paper/4OPFZE4R

@misc{pith2026190802262,
  author       = {Pith},
  title        = {Pith review of: Predicting Prosodic Prominence from Text with Pre-trained Contextualized Word Representations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4OPFZE4R}},
  note         = {Machine review of arXiv:1908.02262}
}

read the original abstract

In this paper we introduce a new natural language processing dataset and benchmark for predicting prosodic prominence from written text. To our knowledge this will be the largest publicly available dataset with prosodic labels. We describe the dataset construction and the resulting benchmark dataset in detail and train a number of different models ranging from feature-based classifiers to neural network systems for the prediction of discretized prosodic prominence. We show that pre-trained contextualized word representations from BERT outperform the other models even with less than 10% of the training data. Finally we discuss the dataset in light of the results and point to future research and plans for further improving both the dataset and methods of predicting prosodic prominence from text. The dataset and the code for the models are publicly available.

Figures

Figures reproduced from arXiv: 1908.02262 by the authors.

**Figure 2.** Test accuracy with different size subsets of the training data for the 2-way classification task. 1 · 10 −2 5 · 10 −2 0.1 0.5 1 0.62 0.64 0.66 0.68 Fraction of training data Accuracy BERT BiLSTM SVM+Glove CRF [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Test accuracy with different size subsets of the training data for the 3-way classification task [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 6.** Figure 6: 2-way classification task confusion matrix for BERT [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 4.** Figure 4: 3-way classification task confusion matrix for BERT [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: 3-way classification task confusion matrix for BiLSTM. In the 3-way classification task, when the gold label is 0 (non prominent) BERT makes more errors with prediction being 2 (very prominent) compared to the BiLSTM model. However, when the gold label is 2 (very prom…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

36 extracted references · 29 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]

Dwight Bolinger. 1972. Accent is predictable (if you're a mind-reader). Language, pages 633--644

work page 1972
[4]

Joan W Bresnan. 1973. Sentence stress and syntactic transformations. In Approaches to natural language, pages 3--47. Springer

work page 1973
[5]

Noam Chomsky and Morris Halle. 1968. The sound pattern of english

work page 1968
[6]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT : Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , pages 4171--4186. A...

work page 2019
[7]

Michelle L Gregory and Yasemin Altun. 2004. Using conditional random fields to predict pitch accents in conversational speech. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics (ACL-2004), page 677. Association for Computational Linguistics

work page 2004
[8]

Julia Hirschberg. 1993. Pitch accent in context predicting intonational prominence from text. Artificial Intelligence, 63(1-2):305--340

1993

Show all 36 references

[9]

Sepp Hochreiter and J\" u rgen Schmidhuber. 1997. Long short-term memory. Neural Comput., 9(8):1735--1780

1997
[10]

Daniel Jurafsky. 1996. A probabilistic model of lexical and syntactic access and disambiguation. Cognitive science, 20(2):137--194

1996
[11]

Daniel Jurafsky, Alan Bell, Michelle Gregory, and William D Raymond. 2001. Probabilistic relations between words: Evidence from reduction in lexical production. Typological studies in language, 45:229--254

2001
[12]

Sofoklis Kakouros, Joris Pelemans, Lyan Verwimp, Patrick Wambacq, and Okko R \"a s \"a nen. 2016. Analyzing the contribution of top-down lexical and bottom-up acoustic cues in the detection of sentence prominence. In INTERSPEECH, pages 1074--1078

2016
[13]

Sofoklis Kakouros and Okko R \"a s \"a nen. 2016. 3pro--an unsupervised method for the automatic detection of sentence prominence in speech. Speech Communication, 82:67--84

2016
[14]

Gina-Anne Levow. 2008. Automatic prosodic labeling with conditional random fields and rich acoustic features. In Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

2008
[15]

Philip Lieberman. 1960. Some acoustic correlates of word stress in american english. The Journal of the Acoustical Society of America, 32(4):451--454

1960
[16]

Erwin Marsi, Martin Reynaert, Antal van den Bosch, Walter Daelemans, and Veronique Hoste. 2003. Learning to predict pitch accents and prosodic boundaries in dutch. In 41st Annual meeting of the Association for Computational Linguistics : proceedings of the conference, pages 48...

2003
[17]

Michael McAuliffe, Michaela Socolof, Sarah Mihuc, Michael Wagner, and Morgan Sonderegger. 2017. M ontreal F orced A ligner: Trainable text-speech alignment using kaldi. In Interspeech, pages 498--502

2017
[18]

Corrado, and Jeffrey Dean

Tomas Mikolov, Kai Chen, Greg S. Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In Proceedings of ICLR

2013
[19]

Yoonsook Mo, Jennifer Cole, and Eun-Kyung Lee. 2008. Na \" ve listeners’ prominence and boundary perception. Proc. Speech Prosody, Campinas, Brazil, pages 735--738

2008
[20]

Thomas Mueller, Helmut Schmid, and Hinrich Sch \"u tze. 2013. Efficient higher-order CRF s for morphological tagging. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 322--332. Association for Computational Linguistics

2013
[21]

Ani Nenkova, Jason Brenier, Anubha Kothari, Sasha Calhoun, Laura Whitton, David Beaver, and Dan Jurafsky. 2007. To memorize or to predict: Prominence labeling in conversational speech. In Proceedings of the Human Language Technology Conference of the North American chapter of ...

2007
[22]

Mari Ostendorf, Patti J Price, and Stefanie Shattuck-Hufnagel. 1995. The B oston U niversity radio news corpus. Linguistic Data Consortium, pages 1--19

1995
[23]

Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur. 2015. Libri S peech: an ASR corpus based on public domain audio books. In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 5206--5210. IEEE

2015
[24]

Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. G lo V e: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ( EMNLP ) , pages 1532--1543. Association for Computational Linguistics

2014
[25]

Asaf Rendel, Raul Fernandez, Ron Hoory, and Bhuvana Ramabhadran. 2016. Using continuous lexical embeddings to improve symbolic-prosody prediction in a text-to-speech front-end. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 565...

2016
[26]

Andrew Rosenberg. 2009. Automatic detection and classification of prosodic events. Columbia University

2009
[27]

Sabrina Stehwien, Ngoc Thang Vu, and Antje Schweitzer. 2018. Effects of word embeddings on neural network-based pitch accent detection. In 9th International Conference on Speech Prosody, pages 719--723

2018
[28]

Karl Stratos and Michael Collins. 2015. Simple semi-supervised POS tagging. In Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

2015
[29]

Antti Suni, Juraj S imko, Daniel Aalto, and Martti Vainio. 2017. Hierarchical representation and estimation of prosody using continuous wavelet transform. Computer Speech & Language, 45:123--136

2017
[30]

Jacques Terken and Dik Hermes. 2000. The perception of prosodic prominence. In Prosody: Theory and experiment, pages 89--127. Springer

2000
[31]

Michael Wagner and Duane G Watson. 2010. Experimental and theoretical advances in prosody: A review. Language and cognitive processes, 25(7-9):905--945

2010
[32]

Dagen Wang and Shrikanth Narayanan. 2007. An acoustic measure for word prominence in spontaneous speech. IEEE transactions on audio, speech, and language processing, 15(2):690--701

2007
[33]

Oliver Watts. 2012. Unsupervised Learning for Text-to-Speech Synthesis. Ph.D. thesis, University of Edinburgh

2012
[34]

Tae-Jin Yoon, Sandra Chavarria, Jennifer Cole, and Mark Hasegawa-Johnson. 2004. Intertranscriber reliability of prosodic labeling on telephone conversation using tobi. In Eighth International Conference on Spoken Language Processing

2004
[35]

Jiahong Yuan, Jason M Brenier, and Daniel Jurafsky. 2005. Pitch accent prediction: Effects of genre and speaker. In Ninth European Conference on Speech Communication and Technology

2005
[36]

Heiga Zen, Viet Dang, Rob Clark, Yu Zhang, Ron J Weiss, Ye Jia, Zhifeng Chen, and Yonghui Wu. 2019. Libri TTS : A corpus derived from L ibri S peech for text-to-speech. arXiv preprint arXiv:1904.02882

2019 arXiv

Pith tools

Reviewed August 14, 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]

Dwight Bolinger. 1972. Accent is predictable (if you're a mind-reader). Language, pages 633--644

work page 1972

[4] [4]

Joan W Bresnan. 1973. Sentence stress and syntactic transformations. In Approaches to natural language, pages 3--47. Springer

work page 1973

[5] [5]

Noam Chomsky and Morris Halle. 1968. The sound pattern of english

work page 1968

[6] [6]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT : Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North A merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , pages 4171--4186. A...

work page 2019

[7] [7]

Michelle L Gregory and Yasemin Altun. 2004. Using conditional random fields to predict pitch accents in conversational speech. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics (ACL-2004), page 677. Association for Computational Linguistics

work page 2004

[8] [8]

Julia Hirschberg. 1993. Pitch accent in context predicting intonational prominence from text. Artificial Intelligence, 63(1-2):305--340

1993