Paper Citation Record · LEDGER

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning

As of 22 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:2506.04527.

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

pith.paper-citation-record.v1
2506.04527 v1

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:45:15.786786Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:45:15.390168Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T10:45:15.943522Z

Reference resolution

49 of 49 outbound references displayed

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External citation measurements

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Outbound references

Observation 8d183f01-308a-4a4d-b3a1-f50a73b0244b · outbound

This paper cites For training high-quality and diverse-styled TTS models, a large amount of text-speech paired data is re- quired [4, 5].

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning For training high-quality and diverse-styled TTS models, a large amount of text-speech paired data is re- quired [4, 5]

Reference 1

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Observation 3828ef2d-8103-4827-9723-0e4ff294770b · outbound

This paper cites Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning

Reference 2

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This paper cites <blank>.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning <blank>

Reference 3

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Observation 283541ee-6f26-4a93-adb5-f166d39e8b93 · outbound

This paper cites Evaluation of proposed annotation model 4.1.1.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Evaluation of proposed annotation model 4.1.1

Reference 4

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Observation dd5fb3c8-6404-4130-b3ef-a50ee30c9db0 · outbound

This paper cites Specifically, we utilized the basic5000 subset along with its manually annotated TTS labels1.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Specifically, we utilized the basic5000 subset along with its manually annotated TTS labels1

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.903592Z

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This paper cites ”, (2) Accent change from low to high “[.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning ”, (2) Accent change from low to high “[

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.884422Z

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Observation 8a7ac17f-3c7a-49fd-b1c8-b4a1f7ba2937 · outbound

This paper cites Applying this method to downstream tasks be- yond textual accent estimation is a challenge for future work.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Applying this method to downstream tasks be- yond textual accent estimation is a challenge for future work

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.865438Z

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Observation 471f62fb-b9d2-4ecb-8445-85cff0353220 · outbound

This paper cites Tacotron: Towards end-to-end speech synthesis,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Tacotron: Towards end-to-end speech synthesis,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.843284Z

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Observation 0d18b20c-8ab5-4230-87dd-921714906ef3 · outbound

This paper cites Fastspeech 2: Fast and high-quality end-to-end text to speech,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Fastspeech 2: Fast and high-quality end-to-end text to speech,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.820773Z

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Observation 76c0dd0c-e7cd-4f32-8924-f033a51a921c · outbound

This paper cites Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.800603Z

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Observation 30c4032d-9850-4d5d-b62e-ae3728234cc1 · outbound

This paper cites Naturalspeech 2: Latent diffusion models are natural and zero-shot speech and singing synthesiz- ers,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Naturalspeech 2: Latent diffusion models are natural and zero-shot speech and singing synthesiz- ers,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.780616Z

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Observation b78bc22a-fcd7-4a2c-84c2-66b3954ae4d2 · outbound

This paper cites Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias

Reference 12

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Observation f942b0ff-5a44-45d4-8b9d-f6c1fa025cb7 · outbound

This paper cites V oicebox: Text-guided multilin- gual universal speech generation at scale,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning V oicebox: Text-guided multilin- gual universal speech generation at scale,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.760835Z

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Observation bc11e927-d6f0-4d57-8504-d2201b043660 · outbound

This paper cites Listening while speaking: Speech chain by deep learning,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Listening while speaking: Speech chain by deep learning,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.741415Z

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Observation 868b8ddd-ff18-4177-8dfd-146abb81dbe7 · outbound

This paper cites Almost unsupervised text to speech and automatic speech recognition,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Almost unsupervised text to speech and automatic speech recognition,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.720787Z

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Observation f8a5af9e-90d8-4ad9-a796-5392a21170be · outbound

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

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Prosodic features con- trol by symbols as input of sequence-to-sequence acoustic mod- eling for neural TTS,

Reference 16

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Observation ef53aded-23e9-4299-9d45-52066e46cc17 · outbound

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

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Investigation of enhanced tacotron text-to-speech synthesis systems with self- attention for pitch accent language,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.678475Z

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Observation c08a13df-5cfb-450e-9d4b-c0447b39af09 · outbound

This paper cites A unified sequence-to-sequence front-end model for mandarin text-to-speech synthesis,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning A unified sequence-to-sequence front-end model for mandarin text-to-speech synthesis,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.646962Z

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Observation 9435527f-a9d1-4d16-9010-aa869e1c7848 · outbound

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

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning A unified accent esti- mation method based on multi-task learning for Japanese text-to- speech,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.619564Z

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Observation a50ed950-a929-4c7e-93a5-db966a4ba857 · outbound

This paper cites Polyphone disambigua- tion and accent prediction using pre-trained language models in Japanese TTS front-end,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Polyphone disambigua- tion and accent prediction using pre-trained language models in Japanese TTS front-end,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.599984Z

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Observation f3392d7b-db93-4477-98bf-158eef3a6723 · outbound

This paper cites Enhancing Japanese text-to-speech ac- curacy with a novel combination Transformer-BERT-based G2P: Integrating pronunciation dictionaries and accent sandhi,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Enhancing Japanese text-to-speech ac- curacy with a novel combination Transformer-BERT-based G2P: Integrating pronunciation dictionaries and accent sandhi,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.570769Z

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Observation f552c249-4b3b-47c8-8c9d-4d3c2c4c0196 · outbound

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

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Audio- conditioned phonemic and prosodic annotation for building text- to-speech models from unlabeled speech data,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.544361Z

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

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Robust speech recognition via large-scale weak supervision,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.517621Z

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Observation c724ebc8-0b5b-4c22-b3c5-84e02602b416 · outbound

This paper cites Reazonspeech: A free and massive corpus for Japanese asr,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Reazonspeech: A free and massive corpus for Japanese asr,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.493668Z

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Observation 6e54d56c-00de-446f-88a5-9df6dee64ba7 · outbound

This paper cites YODAS: Youtube-oriented dataset for audio and speech,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning YODAS: Youtube-oriented dataset for audio and speech,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.471599Z

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Observation 3f41bf25-c650-4f98-9b8a-8920bdf1e30a · outbound

This paper cites OWSM-CTC: An open encoder-only speech foundation model for speech recog- nition, translation, and language identification,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning OWSM-CTC: An open encoder-only speech foundation model for speech recog- nition, translation, and language identification,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.440054Z

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Observation 19463acd-44b6-48c9-86c0-122a72c50bce · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 27

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Observation 5d8ac709-023a-4726-bbaa-09be72f8e728 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T10:45:15.616398Z

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This paper cites PnG BERT: Augmented BERT on phonemes and graphemes for neural TTS,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning PnG BERT: Augmented BERT on phonemes and graphemes for neural TTS,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.411517Z

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Observation 4fb61dce-a47f-449a-bdf0-d4cc80b894ab · outbound

This paper cites Phoneme-level BERT for enhanced prosody of text-to-speech with grapheme pre- dictions,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Phoneme-level BERT for enhanced prosody of text-to-speech with grapheme pre- dictions,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.385283Z

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Observation 2ce861f4-1eea-4496-9484-b587b5dc1434 · outbound

This paper cites Miipher: A robust speech restoration model integrating self-supervised speech and text rep- resentations,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Miipher: A robust speech restoration model integrating self-supervised speech and text rep- resentations,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.366480Z

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Observation 31d5dd8b-a730-4d18-9463-76b826057f19 · outbound

This paper cites Japanese text-to-speech syn- thesis system: Open JTalk,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Japanese text-to-speech syn- thesis system: Open JTalk,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.346905Z

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This paper cites Release of pre-trained mod- els for the Japanese language,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Release of pre-trained mod- els for the Japanese language,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.327855Z

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Observation 583a47e6-ec4b-4d60-9037-fd4384f561c0 · outbound

This paper cites Joint ctc-attention based end- to-end speech recognition using multi-task learning,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Joint ctc-attention based end- to-end speech recognition using multi-task learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.304977Z

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Observation 43022c1b-e346-4bc8-8cde-d01027fbcd08 · outbound

This paper cites Relaxing the conditional indepen- dence assumption of ctc-based asr by conditioning on interme- diate predictions,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Relaxing the conditional indepen- dence assumption of ctc-based asr by conditioning on interme- diate predictions,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.285924Z

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Observation 47071f34-7d6e-493e-900b-270466e0f06d · outbound

This paper cites BERT meets CTC: New formulation of end-to-end speech recognition with pre-trained masked language model,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning BERT meets CTC: New formulation of end-to-end speech recognition with pre-trained masked language model,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.256179Z

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Observation fc735343-fa35-4e9a-a7d3-262905b81062 · outbound

This paper cites Improving speech recognition error prediction for modern and off-the-shelf speech recognizers,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Improving speech recognition error prediction for modern and off-the-shelf speech recognizers,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.236603Z

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Observation b26f4c93-ca02-47c9-bb40-7bb3d732ce36 · outbound

This paper cites The theory of dynamic programming,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning The theory of dynamic programming,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.216220Z

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Observation cbda27c5-89e2-45a0-8e39-e82525343f51 · outbound

This paper cites JSUT and JVS: Free Japanese voice corpora for accelerating speech synthesis re- search,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning JSUT and JVS: Free Japanese voice corpora for accelerating speech synthesis re- search,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.195859Z

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Observation 2059ae9e-9677-44c5-8b28-8d2c822ac4c3 · outbound

This paper cites Applying conditional random fields to Japanese morphological analysis,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Applying conditional random fields to Japanese morphological analysis,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.176497Z

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Observation 5822a7ae-f875-4f4f-8bca-30cb0505e99b · outbound

This paper cites A proper approach to Japanese morphological analysis: Dictionary, model, and eval- uation.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning A proper approach to Japanese morphological analysis: Dictionary, model, and eval- uation

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.151737Z

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Observation eaa20038-d109-494e-84f1-59dd387d048a · outbound

This paper cites Period VITS: Varia- tional inference with explicit pitch modeling for end-to-end emo- tional speech synthesis,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Period VITS: Varia- tional inference with explicit pitch modeling for end-to-end emo- tional speech synthesis,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.129554Z

Source-reported events for the cited work

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This paper cites DEMAND: A col- lection of multi-channel recordings of acoustic noise in diverse environments,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning DEMAND: A col- lection of multi-channel recordings of acoustic noise in diverse environments,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.111311Z

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Observation 1a35e2c5-68bb-42df-8bc1-73b3a4f86434 · outbound

This paper cites The ACE challenge—corpus description and performance evaluation,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning The ACE challenge—corpus description and performance evaluation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.088377Z

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Observation 296c0a6e-4eca-4d0f-9891-b8969d28c888 · outbound

This paper cites End-to-end ASR to jointly predict transcriptions and linguistic annotations,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning End-to-end ASR to jointly predict transcriptions and linguistic annotations,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.069677Z

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Observation 2aaf2e9b-4a5f-415d-b22c-84b3264f98ac · outbound

This paper cites Building competitive direct acoustics-to-word models for english conversa- tional speech recognition,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Building competitive direct acoustics-to-word models for english conversa- tional speech recognition,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.051902Z

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Observation f0633d88-a512-411c-8e16-5b87fe41b9e0 · outbound

This paper cites Joint speech recognition and speaker diarization via sequence transduction,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Joint speech recognition and speaker diarization via sequence transduction,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.028606Z

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Observation 1f62b35d-1388-466c-87de-fbdaad28620a · outbound

This paper cites Uncon- strained many-to-many alignment for automatic pronunciation an- notation,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Uncon- strained many-to-many alignment for automatic pronunciation an- notation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:16.004563Z

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Observation 800a57a2-ed89-4474-9bb9-d7ba9a01ac0e · outbound

This paper cites Evaluation of many-to-many alignment algorithm by auto- matic pronunciation annotation using web text mining,.

Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning Evaluation of many-to-many alignment algorithm by auto- matic pronunciation annotation using web text mining,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:45:15.982204Z

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Pith citing papers

Observation 3828ef2d-8103-4827-9723-0e4ff294770b · inbound