Paper Citation Record · LEDGER

Prosody Labeling with Phoneme-BERT and Speech Foundation Models

As of 19 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2507.03912.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.03912 v1

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:03:18.031484Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:03:14.249592Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T20:03:18.073334Z

Reference resolution

53 of 53 outbound references displayed

verified exact1
verified fuzzy43
unresolved8
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a8e9006e-c63d-4fb7-8a0a-0c41862ebe45 · outbound

This paper cites Prosody Labeling with Phoneme-BERT and Speech Foundation Models.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Prosody Labeling with Phoneme-BERT and Speech Foundation Models

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T20:03:18.076461Z

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Observation 7a37b1f9-63a0-4b0b-b6d1-b84466d39149 · outbound

This paper cites [8] have also conducted automatic prosody an- notation for constructing a speech synthesis database, similar to the objective of our study.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models [8] have also conducted automatic prosody an- notation for constructing a speech synthesis database, similar to the objective of our study

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.570098Z

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Observation 486d9ad2-4af9-4aea-bb4d-14ec30cd5b01 · outbound

This paper cites ashita wa hare desuka.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models ashita wa hare desuka

Reference 3

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Observation 25f7c816-b74d-4ccd-bd15-7b8514a2d4d3 · outbound

This paper cites Figure 3 shows the outline of the proposed model.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Figure 3 shows the outline of the proposed model

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.554087Z

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Observation 7f5ba96d-e444-4a68-8274-f3d160d541e9 · outbound

This paper cites *”: Other symbol that was neither a high-low transition nor a phrase boundary. • “[.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models *”: Other symbol that was neither a high-low transition nor a phrase boundary. • “[

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.546322Z

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Observation d85e9b1d-ba7e-45fe-98fd-6753788a252e · outbound

This paper cites an unresolved cited work.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Unresolved cited work

Reference 6

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Observation ad3aa484-253a-4abe-8ef5-5b8e17d39938 · outbound

This paper cites Automatic prosody annotation with pre-trained text- speech model,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Automatic prosody annotation with pre-trained text- speech model,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.474594Z

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Observation 24699a4f-1edb-44b9-aef4-f4cd2e9745ab · outbound

This paper cites Mora-level prosody prediction for text-to-speech us- ing japanese BERT without accentual labels,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Mora-level prosody prediction for text-to-speech us- ing japanese BERT without accentual labels,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.522972Z

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Observation 91978ff0-5f1a-43d0-8fac-dce5a42e6dec · outbound

This paper cites Semi-supervised prosody model- ing using deep Gaussian process latent variable model,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Semi-supervised prosody model- ing using deep Gaussian process latent variable model,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.515166Z

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Observation 2800bd16-57a5-4d68-b532-08a393ce229a · outbound

This paper cites Ac- cent modeling of low-resourced dialect in pitch accent language using variational autoencoder,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Ac- cent modeling of low-resourced dialect in pitch accent language using variational autoencoder,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.506457Z

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Observation 17c90688-3bf3-4167-b89c-4e05771cc5d0 · outbound

This paper cites Cross-dialect text- to-speech in pitch-accent language incorporating multi-dialect phoneme-level BERT,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Cross-dialect text- to-speech in pitch-accent language incorporating multi-dialect phoneme-level BERT,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.498411Z

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Observation 7752be24-91c1-4332-92de-fe68656049ef · outbound

This paper cites Multi-modal automatic prosody anno- tation with contrastive pretraining of speech-silence and word- punctuation,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Multi-modal automatic prosody anno- tation with contrastive pretraining of speech-silence and word- punctuation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.490401Z

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Observation 9f4f8240-b7e3-4f27-b812-aa1f6842bcc1 · outbound

This paper cites Language-independent prosody-enhanced speech representations for multilingual speech synthesis,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Language-independent prosody-enhanced speech representations for multilingual speech synthesis,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.482741Z

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Observation 8377d84f-481f-43c6-b405-5126743c9a7e · outbound

This paper cites BERT: Pre- training of deep bidirectional transformers for language under- standing,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models BERT: Pre- training of deep bidirectional transformers for language under- standing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.419024Z

Source-reported events for the cited work

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Observation 501b9959-6d09-43de-bc6f-d6a5715126f7 · outbound

This paper cites Audio- conditioned phonemic and prosodic annotation for building text- to-speech models from unlabeled speech data,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Audio- conditioned phonemic and prosodic annotation for building text- to-speech models from unlabeled speech data,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.465831Z

Source-reported events for the cited work

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Observation 02fafbbd-40ca-4ff9-8c6b-9d4d91b2a8c8 · outbound

This paper cites Low-resourced phonetic and prosodic feature estimation with self-supervised-learning-based acoustic modeling,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Low-resourced phonetic and prosodic feature estimation with self-supervised-learning-based acoustic modeling,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.457670Z

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Observation 3a4335c3-5762-4f48-90b7-61b6610ca6c1 · outbound

This paper cites Cross-lingual speech- based ToBI label generation using bidirectional LSTM,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Cross-lingual speech- based ToBI label generation using bidirectional LSTM,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.449384Z

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Observation 994affb5-0eb8-48ed-8edd-474a1541b990 · outbound

This paper cites A unified accent esti- mation method based on multi-task learning for Japanese text-to- speech,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models A unified accent esti- mation method based on multi-task learning for Japanese text-to- speech,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.440586Z

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Observation 73a9539d-6bdd-4c62-bbfc-1831c6547496 · outbound

This paper cites Wav2ToBI: a new approach to automatic ToBI transcription,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Wav2ToBI: a new approach to automatic ToBI transcription,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.432635Z

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Observation 7d985065-12a4-469d-ae23-b642b0469279 · outbound

This paper cites Conformer: Convolution-augmented transformer for speech recognition,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Conformer: Convolution-augmented transformer for speech recognition,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T20:03:15.886329Z

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Observation 75ce9da2-3cf3-4a12-8a6a-6f6eaa5606a5 · outbound

This paper cites Investigation of enhanced Tacotron text-to-speech synthesis systems with self- attention for pitch accent language,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Investigation of enhanced Tacotron text-to-speech synthesis systems with self- attention for pitch accent language,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.361910Z

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Observation 5b927204-f5ad-474e-aaba-5cad605fe1e3 · outbound

This paper cites Robust speech recognition via large-scale weak su- pervision,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Robust speech recognition via large-scale weak su- pervision,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.411280Z

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Observation 01d1492d-4e95-403e-88d1-d12a7e03f4e1 · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models wav2vec 2.0: A framework for self-supervised learning of speech representa- tions,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.403453Z

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Observation e28cb59e-5e18-41cf-9318-cede3a3f7cf9 · outbound

This paper cites Pre-trained text embeddings for enhanced text-to- speech synthesis,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Pre-trained text embeddings for enhanced text-to- speech synthesis,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.395326Z

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Observation 91b6ee43-c70f-4403-9818-071a4513fae6 · outbound

This paper cites Improving prosody with linguistic and BERT derived features in multi-speaker based Mandarin Chinese neural TTS,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Improving prosody with linguistic and BERT derived features in multi-speaker based Mandarin Chinese neural TTS,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.387703Z

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Observation 0596f701-8685-4c3f-93ef-90ef31690aa2 · outbound

This paper cites Improving the prosody of RNN-based English text-to-speech synthesis by incorporating a BERT model,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Improving the prosody of RNN-based English text-to-speech synthesis by incorporating a BERT model,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.379448Z

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Observation e1b73488-3522-47eb-b05f-08f9e55a9403 · outbound

This paper cites Im- proving prosody modelling with cross-utterance BERT embed- dings for end-to-end speech synthesis,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Im- proving prosody modelling with cross-utterance BERT embed- dings for end-to-end speech synthesis,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.371358Z

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Observation cc6a775b-2392-47c7-b5e8-6c6e12926128 · outbound

This paper cites WavLM: Large-scale self-supervised pre-training for full stack speech processing,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models WavLM: Large-scale self-supervised pre-training for full stack speech processing,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.315366Z

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Observation 57503311-5675-44f1-bb7e-bd5a1c06b197 · outbound

This paper cites PE- Wav2vec: A prosody-enhanced speech model for self-supervised prosody learning in TTS,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models PE- Wav2vec: A prosody-enhanced speech model for self-supervised prosody learning in TTS,

Reference 29

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Observation f97fc26c-1ad4-4f1c-8e71-e2eda8f71880 · outbound

This paper cites PnG BERT: Aug- mented BERT on phonemes and graphemes for neural TTS,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models PnG BERT: Aug- mented BERT on phonemes and graphemes for neural TTS,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.344923Z

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This paper cites Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme Predictions.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Phoneme-Level BERT for Enhanced Prosody of Text-to-Speech with Grapheme Predictions

Reference 31

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Observation acfc20ea-a1a6-4a01-8151-3d210e10a9e6 · outbound

This paper cites Detection of prosodic bound- aries in speech using wav2vec 2.0,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Detection of prosodic bound- aries in speech using wav2vec 2.0,

Reference 32

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Observation 0f49b92c-8d03-47be-89cf-189aa850975d · outbound

This paper cites Corpus of Spontaneous Japanese: Its design and evaluation,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Corpus of Spontaneous Japanese: Its design and evaluation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.328914Z

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Observation 060b2b47-c026-4190-bffb-129380289285 · outbound

This paper cites HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,

Reference 34

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This paper cites V AE-based phoneme alignment using gradient an- nealing and SSL acoustic features,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models V AE-based phoneme alignment using gradient an- nealing and SSL acoustic features,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.261266Z

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Observation c7b819f2-d590-4756-a8f6-d33de3c60e10 · outbound

This paper cites Self-supervised speech representation learning: A review,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Self-supervised speech representation learning: A review,

Reference 36

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Observation abc9ed54-b6e4-446c-8c81-fef129ee3414 · outbound

This paper cites StyleTTS 2: Towards human-level text-to-speech through style diffusion and adversarial training with large speech language models,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models StyleTTS 2: Towards human-level text-to-speech through style diffusion and adversarial training with large speech language models,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.300322Z

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This paper cites Emobox: Multilingual multi-corpus speech emotion recognition toolkit and benchmark,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Emobox: Multilingual multi-corpus speech emotion recognition toolkit and benchmark,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.292565Z

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This paper cites Non-intrusive speech intelligibility pre- diction for hearing-impaired users using intermediate ASR fea- tures and human memory models,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Non-intrusive speech intelligibility pre- diction for hearing-impaired users using intermediate ASR fea- tures and human memory models,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.284318Z

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Observation 0b479e7d-188f-4172-bba5-2fae76a05cfe · outbound

This paper cites Montreal forced aligner: Trainable text-speech align- ment using kaldi.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Montreal forced aligner: Trainable text-speech align- ment using kaldi

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.276771Z

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Observation 36e6563f-7f5b-4834-8ede-aff580a51a1c · outbound

This paper cites One TTS alignment to rule them all,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models One TTS alignment to rule them all,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.269135Z

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Observation 6fe82536-100d-48da-b81a-b35f370b1b9a · outbound

This paper cites X-JToBI: an extended J-ToBI for spontaneous speech,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models X-JToBI: an extended J-ToBI for spontaneous speech,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.207838Z

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Observation 1e7d9279-efec-43b0-a8ed-86e58662a4a5 · outbound

This paper cites Stress in Thai,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Stress in Thai,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.253843Z

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Observation 30199321-7b5d-41a0-9bdd-cedac8f77223 · outbound

This paper cites ee shumi ga ongaku nandesu keredomo.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models ee shumi ga ongaku nandesu keredomo

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.538756Z

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Observation 4a37a47a-7458-4da5-87f0-7374f43f504d · outbound

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Prosody Labeling with Phoneme-BERT and Speech Foundation Models Unresolved cited work

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:03:18.246286Z

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Observation ee997eda-f97a-411d-add7-3492446b1997 · outbound

This paper cites an unresolved cited work.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Unresolved cited work

Reference 46

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:03:18.238744Z

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Observation 5e13959f-06c3-468c-90a2-24e9a228116f · outbound

This paper cites an unresolved cited work.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:03:18.231361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1043fdb1-3323-4995-85ab-f70300f9fcd1 · outbound

This paper cites Adam: A method for stochastic opti- mization,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Adam: A method for stochastic opti- mization,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.223716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 03c44fd4-d47f-4fac-88aa-b3e519a329b4 · outbound

This paper cites Prosodic features con- trol by symbols as input of sequence-to-sequence acoustic mod- eling for neural TTS,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Prosodic features con- trol by symbols as input of sequence-to-sequence acoustic mod- eling for neural TTS,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.216152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ed323aca-ccbe-48df-8dd5-3fdc755c5c3f · outbound

This paper cites ESPnet: End-to-end speech processing toolkit,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models ESPnet: End-to-end speech processing toolkit,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.199524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7a77b45e-0bef-43fa-a63d-dde25d8ad60f · outbound

This paper cites an unresolved cited work.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:03:18.191205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4a17447c-9180-42b4-a351-1ad51b06dae9 · outbound

This paper cites an unresolved cited work.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:03:18.092629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 566d1044-1005-4142-a880-81b694d682dd · outbound

This paper cites WORLD: A vocoder- based high-quality speech synthesis system for real-time appli- cations,.

Prosody Labeling with Phoneme-BERT and Speech Foundation Models WORLD: A vocoder- based high-quality speech synthesis system for real-time appli- cations,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.084938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation a8e9006e-c63d-4fb7-8a0a-0c41862ebe45 · inbound