# VoiceLDM: Text-to-Speech with Environmental Context

arXiv: https://arxiv.org/abs/2309.13664 · HTML: https://mlsanity.com/paper/2309.13664/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 29 references that are in the map.
- First posted on arXiv 24 Sep 2023.

## Builds on (most cited first, 26 of 26)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [AudioLM: a Language Modeling Approach to Audio Generation](https://mlsanity.com/paper/2209.03143/) (2022)
- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining](https://mlsanity.com/paper/2308.05734/) (2023)
- [Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models](https://mlsanity.com/paper/2301.12661/) (2023)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [Diffsound: Discrete Diffusion Model for Text-to-sound Generation](https://mlsanity.com/paper/2207.09983/) (2022)
- [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://mlsanity.com/paper/2110.07205/) (2021)
- [NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality](https://mlsanity.com/paper/2205.04421/) (2022)
- [Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model](https://mlsanity.com/paper/2304.13731/) (2023)
- [PromptTTS: Controllable Text-to-Speech with Text Descriptions](https://mlsanity.com/paper/2211.12171/) (2022)
- [InstructTTS: Modelling Expressive TTS in Discrete Latent Space with Natural Language Style Prompt](https://mlsanity.com/paper/2301.13662/) (2023)
- [Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation](https://mlsanity.com/paper/2305.18474/) (2023)
- [PromptStyle: Controllable Style Transfer for Text-to-Speech with Natural Language Descriptions](https://mlsanity.com/paper/2305.19522/) (2023)
- [Expressive Text-to-Speech using Style Tag](https://mlsanity.com/paper/2104.00436/) (2021)
- [Enhanced Controllability of Diffusion Models via Feature Disentanglement and Realism-Enhanced Sampling Methods](https://mlsanity.com/paper/2302.14368/) (2023)

## Built on by (most cited first, 23 of 23)

- [MegaTTS 3: Sparse Alignment Enhanced Latent Diffusion Transformer for Zero-Shot Speech Synthesis](https://mlsanity.com/paper/2502.18924/) (2025)
- [DiTTo-TTS: Diffusion Transformers for Scalable Text-to-Speech without Domain-Specific Factors](https://mlsanity.com/paper/2406.11427/) (2024)
- [Towards Controllable Speech Synthesis in the Era of Large Language Models: A Systematic Survey](https://mlsanity.com/paper/2412.06602/) (2024)
- [CoVoMix2: Advancing Zero-Shot Dialogue Generation with Fully Non-Autoregressive Flow Matching](https://mlsanity.com/paper/2506.00885/) (2025)
- [ViSAGe: Video-to-Spatial Audio Generation](https://mlsanity.com/paper/2506.12199/) (2025)
- [ControlAudio: Tackling Text-Guided, Timing-Indicated and Intelligible Audio Generation via Progressive Diffusion Modeling](https://mlsanity.com/paper/2510.08878/) (2025)
- [VoiceDiT: Dual-Condition Diffusion Transformer for Environment-Aware Speech Synthesis](https://mlsanity.com/paper/2412.19259/) (2024)
- [MMMG: a Comprehensive and Reliable Benchmark for Multitask Multimodal Generation](https://mlsanity.com/paper/2505.17613/) (2025)
- [Multi-Source Spatial Knowledge Understanding for Immersive Visual Text-to-Speech](https://mlsanity.com/paper/2410.14101/) (2024)
- [Multi-modal and Multi-scale Spatial Environment Understanding for Immersive Visual Text-to-Speech](https://mlsanity.com/paper/2412.11409/) (2024)
- [Selective Classifier-free Guidance for Zero-shot Text-to-speech](https://mlsanity.com/paper/2509.19668/) (2025)
- [UmbraTTS: Adapting Text-to-Speech to Environmental Contexts with Flow Matching](https://mlsanity.com/paper/2506.09874/) (2025)
- [In-the-wild Audio Spatialization with Flexible Text-guided Localization](https://mlsanity.com/paper/2506.00927/) (2025)
- [MaskVCT: Masked Voice Codec Transformer for Zero-Shot Voice Conversion With Increased Controllability via Multiple Guidances](https://mlsanity.com/paper/2509.17143/) (2025)
- [ParaMETA: Towards Learning Disentangled Paralinguistic Speaking Styles Representations from Speech](https://mlsanity.com/paper/2601.12289/) (2026)
- [OmniSonic: Towards Universal and Holistic Audio Generation from Video and Text](https://mlsanity.com/paper/2604.04348/) (2026)
- [Joint Residual Reweighting for Classifier Free Guidance in Flow-Matching Zero-Shot TTS](https://mlsanity.com/paper/2606.25672/) (2026)
- [InfiniteAudio: Infinite-Length Audio Generation with Consistency](https://mlsanity.com/paper/2506.03020/) (2025)
- [VividVoice: A Unified Framework for Scene-Aware Visually-Driven Speech Synthesis](https://mlsanity.com/paper/2602.02591/) (2026)
- [Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts](https://mlsanity.com/paper/2605.28063/) (2026)
- [ImmersiveTTS: Environment-Aware Text-to-Speech with Multimodal Diffusion Transformer and Domain-Specific Representation Alignment](https://mlsanity.com/paper/2605.30965/) (2026)
- [Towards Real-world Environment-aware Zero-shot Text-to-speech Synthesis via Disentangled Audio Infilling](https://mlsanity.com/paper/2608.03011/) (2026)
- [Learnable Classifier-Free Guidance Null Embeddings for Enhanced Controllable Speech Synthesis](https://mlsanity.com/paper/2609.25411/) (2026)

## Abstract (arXiv)

This paper presents VoiceLDM, a model designed to produce audio that accurately follows two distinct natural language text prompts: the description prompt and the content prompt. The former provides information about the overall environmental context of the audio, while the latter conveys the linguistic content. To achieve this, we adopt a text-to-audio (TTA) model based on latent diffusion models and extend its functionality to incorporate an additional content prompt as a conditional input. By utilizing pretrained contrastive language-audio pretraining (CLAP) and Whisper, VoiceLDM is trained on large amounts of real-world audio without manual annotations or transcriptions. Additionally, we employ dual classifier-free guidance to further enhance the controllability of VoiceLDM. Experimental results demonstrate that VoiceLDM is capable of generating plausible audio that aligns well with both input conditions, even surpassing the speech intelligibility of the ground truth audio on the AudioCaps test set. Furthermore, we explore the text-to-speech (TTS) and zero-shot text-to-audio capabilities of VoiceLDM and show that it achieves competitive results. Demos and code are available at https://voiceldm.github.io.