Paper Citation Record · LEDGER

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment

As of 14 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2505.19595.

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

pith.paper-citation-record.v1
2505.19595 v2

Coverage vector

measured 45 of 45 reference resolution

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measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:15:52.829136Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:15:55.827429Z

Reference resolution

45 of 45 outbound references displayed

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External citation measurements

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Outbound references

Observation bb7fc26a-ef85-4b17-9cd1-4f8198f10e5c · outbound

This paper cites an unresolved cited work.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Unresolved cited work

Reference 1

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Observation 7613c17a-498e-42a3-8d17-dd44120f1d7c · outbound

This paper cites Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment

Reference 2

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Observation 01dc9593-8e57-4e1e-a048-8da73aba0b7c · outbound

This paper cites Dataset We train our model on the LibriTTS [31] dataset, a multi- speaker English corpus containing approximately 585 hours of read speech sampled at 24 kHz.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Dataset We train our model on the LibriTTS [31] dataset, a multi- speaker English corpus containing approximately 585 hours of read speech sampled at 24 kHz

Reference 3

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Observation 702f54ac-37a2-4adc-a8b5-3a746a42d4ac · outbound

This paper cites speech align.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment speech align

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:01.618499Z

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Observation 0faadb96-b8ce-493a-b09d-7274d9b0a12a · outbound

This paper cites This ap- proach significantly accelerates model convergence while en- hancing the quality of generated speech.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment This ap- proach significantly accelerates model convergence while en- hancing the quality of generated speech

Reference 5

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Observation 98f04b34-3288-47ce-a147-dd04790540d3 · outbound

This paper cites In future work, we plan to further op- timize the framework by reducing model complexity and accel- erating inference.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment In future work, we plan to further op- timize the framework by reducing model complexity and accel- erating inference

Reference 6

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Observation 258d8797-2481-4966-a231-dbe012515b96 · outbound

This paper cites Choi, J.-H.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Choi, J.-H

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:01.152594Z

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Observation 6cc1c89c-7940-405c-8b17-9a854be9677f · outbound

This paper cites Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

Reference 8

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Observation 1d4d6018-6c07-457a-8bcd-920d8e4b9f3c · outbound

This paper cites Seed-TTS: A Family of High-Quality Versatile Speech Generation Models.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Seed-TTS: A Family of High-Quality Versatile Speech Generation Models

Reference 9

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Observation d2e23561-cb4c-4429-b577-f92f37f4fae7 · outbound

This paper cites V oicecraft: Zero-shot speech editing and text-to-speech in the wild,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment V oicecraft: Zero-shot speech editing and text-to-speech in the wild,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:00.980493Z

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Observation e6714b00-7472-44e4-8dd7-2f684454f6c5 · outbound

This paper cites BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data

Reference 11

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Observation 94639417-d75f-4193-b59b-dd326c03cc02 · outbound

This paper cites Autoregressive Speech Synthesis without Vector Quantization.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Autoregressive Speech Synthesis without Vector Quantization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:15:53.849366Z

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Observation e41f997a-0485-4e23-98ed-f51d6bd54884 · outbound

This paper cites V oice- box: Text-guided multilingual universal speech generation at scale,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment V oice- box: Text-guided multilingual universal speech generation at scale,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:00.771048Z

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Observation 27bab5ff-4fe7-4713-8a79-6587a2a54bbc · outbound

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

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Naturalspeech 2: Latent diffusion models are natural and zero-shot speech and singing synthesizers,

Reference 14

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Observation 5c1f7394-f16b-432a-8e9c-fe282429a3cf · outbound

This paper cites E2 tts: Embarrassingly easy fully non-autoregressive zero-shot tts,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment E2 tts: Embarrassingly easy fully non-autoregressive zero-shot tts,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:00.429665Z

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Observation 9596f8ff-8a8d-4e9a-8684-6b308bf5e031 · outbound

This paper cites F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching

Reference 16

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Observation c507b2b2-efe6-46d7-8b42-232f6c1a8f0c · outbound

This paper cites Ditto-tts: Efficient and scalable zero-shot text-to-speech with diffusion transformer,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Ditto-tts: Efficient and scalable zero-shot text-to-speech with diffusion transformer,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:16:00.150992Z

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Observation 5f3b1eba-920e-48f8-b4c0-9c14da64bfbe · outbound

This paper cites Accelerating codec-based speech synthesis with multi- token prediction and speculative decoding,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Accelerating codec-based speech synthesis with multi- token prediction and speculative decoding,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:59.913822Z

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Observation 578d911b-13af-4ec7-899d-516c8e495232 · outbound

This paper cites VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment VALL-E R: Robust and Efficient Zero-Shot Text-to-Speech Synthesis via Monotonic Alignment

Reference 19

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Observation 37e878e4-81ed-4917-b258-be620ad63813 · outbound

This paper cites Vall-t: Decoder-only generative transducer for robust and decoding-controllable text-to-speech,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Vall-t: Decoder-only generative transducer for robust and decoding-controllable text-to-speech,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:59.607420Z

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Observation e4889d69-d0b6-4e18-94b5-6db5a6357eba · outbound

This paper cites Flow matching for generative modeling,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Flow matching for generative modeling,

Reference 21

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Observation 8e022587-4e6e-4444-8ab2-63d0c33e41af · outbound

This paper cites Scalable diffusion models with transform- ers,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Scalable diffusion models with transform- ers,

Reference 22

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This paper cites Efficient diffusion training via min-snr weighting strat- egy,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Efficient diffusion training via min-snr weighting strat- egy,

Reference 23

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Observation 7e709a39-c473-4410-b4e4-20bf79475049 · outbound

This paper cites Fasterdit: Towards faster diffusion transformers training without architecture modification,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Fasterdit: Towards faster diffusion transformers training without architecture modification,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:59.205251Z

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Observation e9e50506-c6bb-495b-98d0-e26ff9e6bf4d · outbound

This paper cites Representation alignment for generation: Training diffu- sion transformers is easier than you think,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Representation alignment for generation: Training diffu- sion transformers is easier than you think,

Reference 25

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Observation c2174071-aecc-4622-8714-9a44830d98e4 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment U-net: Convolutional networks for biomedical image segmentation,

Reference 26

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Observation 8d6618dd-846d-4943-9b46-f4f7c3b948a5 · outbound

This paper cites Attention is all you need,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Attention is all you need,

Reference 27

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Observation a3b9ff3e-2fcf-4bf6-b73e-6b2a7743199f · outbound

This paper cites Con- nectionist temporal classification: Labelling unsegmented se- quence data with recurrent neural networks,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Con- nectionist temporal classification: Labelling unsegmented se- quence data with recurrent neural networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:58.617541Z

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Observation 9f3a332f-2942-4462-a4da-9d66d61fe7de · outbound

This paper cites Intermediate loss regularization for ctc- based speech recognition,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Intermediate loss regularization for ctc- based speech recognition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:58.310597Z

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Observation 64a576d3-3564-423d-a154-bf4847c85770 · outbound

This paper cites Audio-visual efficient conformer for robust speech recognition,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Audio-visual efficient conformer for robust speech recognition,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:58.067644Z

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Observation 12af8411-e6e6-4a18-afc5-e109460d64a5 · outbound

This paper cites Speechtok- enizer: Unified speech tokenizer for speech large language mod- els,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Speechtok- enizer: Unified speech tokenizer for speech large language mod- els,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:57.827126Z

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Observation dfce062a-4b05-4457-ad8e-f42ebb911f27 · outbound

This paper cites Naturalspeech 3: Zero-shot speech synthesis with factorized codec and diffusion models,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Naturalspeech 3: Zero-shot speech synthesis with factorized codec and diffusion models,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:57.646947Z

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Observation afad2a94-9925-4ab6-bb5f-c3a1503d52ce · outbound

This paper cites Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Hubert: Self-supervised speech represen- tation learning by masked prediction of hidden units,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:57.497847Z

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Observation 803692c3-015b-466b-8cbe-2b7465842a7f · outbound

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

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Wavlm: Large-scale self- supervised pre-training for full stack speech processing,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:57.264673Z

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Observation 9d8afcab-636c-4abe-8b1d-ef87767ad15e · outbound

This paper cites W2v-bert: Combining contrastive learning and masked language modeling for self-supervised speech pre-training,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment W2v-bert: Combining contrastive learning and masked language modeling for self-supervised speech pre-training,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:57.123506Z

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Observation e3641322-96fe-4ba8-8d41-73fac6cef310 · outbound

This paper cites CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens

Reference 36

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Observation 594d5d61-61ff-4221-becd-05815f436794 · outbound

This paper cites FireRedTTS: A Foundation Text-To-Speech Framework for Industry-Level Generative Speech Applications.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment FireRedTTS: A Foundation Text-To-Speech Framework for Industry-Level Generative Speech Applications

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T14:15:55.201763Z

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Observation 35f5bfe1-7bfc-40ea-98c9-3231adacf766 · outbound

This paper cites Libritts: A corpus derived from librispeech for text- to-speech,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Libritts: A corpus derived from librispeech for text- to-speech,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:56.922439Z

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Observation 80e28793-afc4-4762-8796-e84d592efcdd · outbound

This paper cites Lib- rispeech: An asr corpus based on public domain audio books,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Lib- rispeech: An asr corpus based on public domain audio books,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:56.795579Z

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Observation 697a294c-cd83-46f6-bcda-2ebe01d793fb · outbound

This paper cites Convnext v2: Co-designing and scaling convnets with masked autoencoders,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Convnext v2: Co-designing and scaling convnets with masked autoencoders,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:56.689787Z

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Observation 53c028ee-ac48-4ef9-be5f-9f625d05ba04 · outbound

This paper cites Decoupled weight decay regulariza- tion,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Decoupled weight decay regulariza- tion,

Reference 41

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Source-reported events for the cited work

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Observation f1a62001-924d-4777-95bb-7a89c40b6917 · outbound

This paper cites V ocos: Closing the gap between time-domain and fourier-based neural vocoders for high-quality audio synthesis,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment V ocos: Closing the gap between time-domain and fourier-based neural vocoders for high-quality audio synthesis,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:56.516535Z

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Observation 03fb12a6-8584-435b-a51b-19c107223dbd · outbound

This paper cites Librispeech-pc: Benchmark for evaluation of punctuation and capitalization capabilities of end-to-end asr mod- els,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Librispeech-pc: Benchmark for evaluation of punctuation and capitalization capabilities of end-to-end asr mod- els,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:56.364968Z

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Observation f1d8e692-c73e-4f5f-b7e5-6a6bb1df7fa6 · outbound

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

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Robust speech recognition via large-scale weak su- pervision,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:56.244826Z

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Observation 9292437f-a354-4340-a186-9c6b2b4d6cf5 · outbound

This paper cites Utmos: Utokyo-sarulab system for voicemos challenge 2022,.

Accelerating Diffusion-based Text-to-Speech Model Training with Dual Modality Alignment Utmos: Utokyo-sarulab system for voicemos challenge 2022,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:15:56.033418Z

Source-reported events for the cited work

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Pith citing papers

Observation 7613c17a-498e-42a3-8d17-dd44120f1d7c · inbound