Paper Citation Record · LEDGER

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts

As of 7 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2508.11326.

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

pith.paper-citation-record.v1
2508.11326 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T20:04:51.686582Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T10:28:18.202974Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T12:15:01.137692Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 9738eb00-04f2-48bf-92c0-b27cd17b3e05 · outbound

This paper cites URL https://elevenlabs.io/.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts URL https://elevenlabs.io/

Reference 1

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Observation ce9e6f66-ac65-4b5c-9169-07ff32a861b1 · outbound

This paper cites URL https://www.minimaxi.com/.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts URL https://www.minimaxi.com/

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:04:55.589041Z

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Observation 89f4c621-ff5f-4dcc-822d-b331d18b5808 · outbound

This paper cites Prompttts: Controllable text-to-speech with text descriptions.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Prompttts: Controllable text-to-speech with text descriptions

Reference 3

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Observation 8c8023d0-3447-4634-b9fe-92b3eadf8ecc · outbound

This paper cites Textrolspeech: A text style control speech corpus with codec language text-to-speech models.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Textrolspeech: A text style control speech corpus with codec language text-to-speech models

Reference 5

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This paper cites Prompttts 2: Describing and generating voices with text prompt.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Prompttts 2: Describing and generating voices with text prompt

Reference 6

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Observation 1bdee4dd-a422-45a5-97ce-36ccaa271d3e · outbound

This paper cites Libritts-p: A corpus with speaking style and speaker identity prompts for text- to-speech and style captioning.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Libritts-p: A corpus with speaking style and speaker identity prompts for text- to-speech and style captioning

Reference 7

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Observation 7fc63fbb-c694-47ce-96fa-7e5e5aa45525 · outbound

This paper cites Speechcraft: A fine-grained expressive speech dataset with natural language description.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Speechcraft: A fine-grained expressive speech dataset with natural language description

Reference 8

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This paper cites Natural language guidance of high-fidelity text-to-speech with synthetic annotations.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Natural language guidance of high-fidelity text-to-speech with synthetic annotations

Reference 9

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This paper cites Scaling rich style-prompted text-to-speech datasets.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Scaling rich style-prompted text-to-speech datasets

Reference 10

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This paper cites GPT-4 Technical Report.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts GPT-4 Technical Report

Reference 11

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Observation b68494d3-07d7-4dba-a3df-bf733c33b7f2 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 12

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Unresolved cited work

Reference 13

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This paper cites CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models

Reference 14

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Observation e8030ec9-5c4e-4c8b-b9de-b51f1d09328a · outbound

This paper cites EmoVoice: LLM-based Emotional Text-To-Speech Model with Freestyle Text Prompting.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts EmoVoice: LLM-based Emotional Text-To-Speech Model with Freestyle Text Prompting

Reference 15

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This paper cites Qwen3 Technical Report.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Qwen3 Technical Report

Reference 16

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Observation bf9a4a57-a755-4743-aae5-8ba47bbd97e6 · outbound

This paper cites Investigating the Catastrophic Forgetting in Multimodal Large Language Models.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Investigating the Catastrophic Forgetting in Multimodal Large Language Models

Reference 17

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Observation d8fb27d0-048f-43b8-8811-f6ae30b3d965 · outbound

This paper cites Mono-internvl: Pushing the boundaries of monolithic multimodal large language models with endogenous visual pre-training.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Mono-internvl: Pushing the boundaries of monolithic multimodal large language models with endogenous visual pre-training

Reference 18

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Investigating the Catastrophic Forgetting in Multimodal Large Language Models

Reference 19

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Vlmo: Unified vision- language pre-training with mixture-of-modality-experts

Reference 20

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This paper cites EVEv2: Improved Baselines for Encoder-Free Vision-Language Models.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts EVEv2: Improved Baselines for Encoder-Free Vision-Language Models

Reference 21

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This paper cites Simbert: Integrating retrieval and generation into bert.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Simbert: Integrating retrieval and generation into bert

Reference 22

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This paper cites Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

Reference 23

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This paper cites Fastspeech: Fast, robust and controllable text to speech.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Fastspeech: Fast, robust and controllable text to speech

Reference 24

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Observation faa15446-d0e2-479a-84e9-03b0b46f337b · outbound

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

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

Reference 25

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts BERT: pre-training of deep bidirectional transformers for language understanding

Reference 26

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts MiniMax-Speech: Intrinsic Zero-Shot Text-to-Speech with a Learnable Speaker Encoder

Reference 27

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Scaling vision-language models with sparse mixture of experts

Reference 28

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis

Reference 29

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

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Seed-TTS: A Family of High-Quality Versatile Speech Generation Models

Reference 30

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts The Llama 3 Herd of Models

Reference 31

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This paper cites Orpheus tts: Towards human-sounding tts.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Orpheus tts: Towards human-sounding tts

Reference 32

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

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Hubert: Self-supervised speech representation learning by masked prediction of hidden units

Reference 33

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Qwen2.5-1M Technical Report

Reference 34

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Neural discrete representation learning

Reference 35

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Soundstream: An end-to-end neural audio codec

Reference 36

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts High-fidelity audio compression with improved RVQGAN

Reference 37

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens

Reference 38

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Self-supervised learning with random-projection quantizer for speech recognition

Reference 39

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Parker, CJ Carr, Zack Zukowski, Josiah Taylor, and Jordi Pons

Reference 40

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Albergo, Nicholas M

Reference 41

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Emilia: A large-scale, extensive, multilingual, and diverse dataset for speech generation

Reference 42

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This paper cites Elucidating the de- sign space of diffusion-based generative models.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Elucidating the de- sign space of diffusion-based generative models

Reference 44

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This paper cites URL https://doi.org/10.1109/TASLP.2021.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts URL https://doi.org/10.1109/TASLP.2021

Reference 2021

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MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks

Reference 2022

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This paper cites URL https://doi.org/10.1109/ ICASSP49357.2023.10096285.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts URL https://doi.org/10.1109/ ICASSP49357.2023.10096285

Reference 2023

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This paper cites doi: 10.18653/V1/N19-1423.

MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts doi: 10.18653/V1/N19-1423

Reference 4186

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Pith citing papers

Observation 5c51c5e4-1523-44d3-a286-74cf8ac7a6d0 · inbound

Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts cites this paper.

Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts MoE-TTS: Enhancing Out-of-Domain Text Understanding for Description-based TTS via Mixture-of-Experts

Reference 21

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