ImmersiveTTS: Joint Speech & Environment Synthesis

• ImmersiveTTS is an environment-aware TTS system that jointly generates transcript-accurate speech and semantically consistent environmental audio.
• It employs dual processing pathways and a multimodal diffusion transformer to align isolated speech content with ambient acoustic cues.
• Empirical results show enhanced naturalness, intelligibility, and audio fidelity, largely driven by domain-specific representation alignment.

Searching arXiv for papers on ImmersiveTTS and closely related environment-aware / visual / spatial TTS. ImmersiveTTS is an environment-aware text-to-speech model that generates natural speech seamlessly integrated within environmental contexts by explicitly modeling cross-modal interactions (Yun et al., 29 May 2026). In the broader research lineage, it belongs to a class of systems that treat acoustic environment as a first-class conditioning factor in speech synthesis rather than as nuisance variability to be removed, extending earlier environment-aware and visual TTS work that modeled room reverberation, scene images, depth, and semantic scene prompts as controls on synthesized speech (Tan et al., 2021).

1. Definition and scope

ImmersiveTTS addresses a joint generation problem: it must preserve transcript fidelity and speaker characteristics while also generating environmental acoustics that sound semantically appropriate and naturally blended with the speech (Yun et al., 29 May 2026). This differs from conventional TTS, which typically focuses on linguistic correctness, pronunciation, duration, prosody, and speaker identity, and from generic text-to-audio generation, which can generate diverse sound effects or scenes but is not optimized for the fine temporal and phonetic precision needed for intelligible speech (Yun et al., 29 May 2026).

Within this literature, “immersive” has not had a single fixed meaning. In earlier environment-aware TTS, the environment was defined narrowly as room reverberation represented by room impulse responses, with noise explicitly left for future work (Tan et al., 2021). In visual TTS, immersion usually meant scene-aware reverberant speech consistent with an environmental image rather than full binaural rendering or 6DoF spatial audio (Liu et al., 2023). ImmersiveTTS broadens this scope by targeting joint speech-plus-environment generation inside a unified latent model, rather than reverberation transfer alone (Yun et al., 29 May 2026).

A central difficulty is that speech and environmental audio have very different acoustic patterns and temporal dynamics. Speech requires precise phoneme timing and transcript alignment, whereas environmental audio often contains diffuse or non-stationary textures whose semantics are scene-level rather than phoneme-level (Yun et al., 29 May 2026). The model is therefore designed around separate but interacting representations for transcript-aligned speech content and text-conditioned environmental context.

2. Research lineage

A direct precursor is "Environment Aware Text-to-Speech Synthesis" (Tan et al., 2021), which treated acoustic environment as a controllable factor alongside speaker identity. That system trained separate speaker and environment embedding extractors and conditioned a TTS decoder on text, speaker embedding, and environment embedding, showing that speaker and room characteristics could be recombined for unseen speaker-environment pairings. Its notion of environment, however, was restricted primarily to reverberation.

"ViT-TTS: Visual Text-to-Speech with Scalable Diffusion Transformer" (Liu et al., 2023) moved the field toward immersive scene grounding by conditioning TTS on environmental images. It defined visual TTS as generating speech from text plus a target-environment image so that the audio includes reverberation effects appropriate to the scene, and it used a diffusion transformer denoiser rather than a conventional convolutional backbone. Later visual systems expanded the environmental signal beyond global RGB features. "Multi-Source Spatial Knowledge Understanding for Immersive Visual Text-to-Speech" (He et al., 2024) added depth, speaker position knowledge from object detection, and Gemini-generated semantic captions through a dominant-supplement serial interaction mechanism, while "Multi-modal and Multi-scale Spatial Environment Understanding for Immersive Visual Text-to-Speech" (Liu et al., 2024) combined RGB, depth, local patch selection, and local-aware global fusion for room-acoustic inference.

"I2TTS: Image-indicated Immersive Text-to-speech Synthesis with Spatial Perception" (Zhang et al., 2024) further framed immersive TTS as a multimodal problem with a scene prompt encoder and a reverberation classification and refinement technique that adjusts the synthesized mel-spectrogram so that the reverberation condition matches the scene accurately. In parallel, systems work on conversational VR such as "Let's Give a Voice to Conversational Agents in Virtual Reality" (Yin et al., 2023) clarified how TTS fits into embodied pipelines with Unity, avatar lip sync, and latency-sensitive turn taking, even though they did not propose a new spatial TTS model.

This lineage places ImmersiveTTS at the intersection of environment-aware TTS, visual/scene-conditioned TTS, and general multimodal audio generation. What distinguishes it is the claim that speech and environment should be co-produced through explicit cross-modal interaction rather than synthesized separately and mixed afterward (Yun et al., 29 May 2026).

3. Architectural organization

ImmersiveTTS uses three main inputs: a content prompt $y_{\text{cont}}$, an environment prompt $y_{\text{env}}$, and a speaker prompt from which a speaker embedding is extracted (Yun et al., 29 May 2026). The model operates not on raw waveform directly but in the latent space of a pretrained AudioLDM2 VAE. A waveform $X_{\text{wav}}$ is converted to a log-mel spectrogram $X_{\text{mel}} \in \mathbb{R}^{F \times L}$, and the frozen VAE encoder compresses it to $Z \in \mathbb{R}^{8 \times F/4 \times L/4}$ (Yun et al., 29 May 2026).

The speech pathway is transcript-aligned. A text encoder converts the content prompt into hidden states, monotonic alignment search estimates durations $d'_{1:L}$, and the hidden vectors are expanded according to $d'$ to obtain a frame-level prior mel representation $\mu$ (Yun et al., 29 May 2026). Because $\mu$ is mel-like while the generator works in AudioLDM2 latent space, a convolutional network bridges this mismatch; the resulting feature is concatenated with the noisy latent $Z_t$ along the channel dimension and enters the speech stream (Yun et al., 29 May 2026). This design imports a TTS-style alignment prior into a general audio-latent generator.

The environment pathway uses dual granularity. The environment prompt is encoded by CLAP into a global embedding that is projected and combined with timestep embedding to condition AdaLN parameters, while the last hidden states of Flan-T5-Large are projected as token-level environmental features for an environment context stream (Yun et al., 29 May 2026). CLAP therefore provides coarse scene-level semantics, and Flan-T5 provides local token-level detail.

The core generator is a multimodal diffusion transformer adapted from the SD3/Flux family. It uses 12 double-stream DiT blocks followed by 18 single-stream DiT blocks, with hidden size 1024, 6 attention heads, and about 450M trainable parameters (Yun et al., 29 May 2026). In the double-stream stage, the environment context stream and the speech stream communicate through joint attention; after that, only the speech stream is retained for further refinement in the single-stream blocks (Yun et al., 29 May 2026). The model’s defining claim is that explicit joint attention allows speech and background to influence one another during generation rather than coexist only after mixing.

4. Training objectives, representation alignment, and inference

ImmersiveTTS uses flow matching rather than standard diffusion. The latent trajectory is defined by

$y_{\text{env}}$0

with straight-line interpolation

$y_{\text{env}}$1

and the flow loss

$y_{\text{env}}$2

(Yun et al., 29 May 2026). This objective trains the multimodal diffusion transformer to predict the velocity field in latent space.

A major methodological contribution is domain-specific representation alignment. The model aligns intermediate speech-stream features to frozen self-supervised teachers: WavLM-Large for speech and ATST-Frame-Base for environmental audio (Yun et al., 29 May 2026). WavLM is computed on clean speech from LibriTTS before mixing, whereas ATST-Frame is computed on mixed speech-plus-environment audio (Yun et al., 29 May 2026). For each teacher, the cosine-similarity objective is

$y_{\text{env}}$3

and the total alignment term is

$y_{\text{env}}$4

with $y_{\text{env}}$5 in experiments (Yun et al., 29 May 2026). The full training objective is a weighted sum of prior loss, duration loss, flow loss, and REPA, with all weights set to 1 (Yun et al., 29 May 2026).

The training corpus is synthetically constructed by mixing LibriTTS train-clean-360 speech with WavCaps non-speech environmental audio (Yun et al., 29 May 2026). WavCaps contains 400k audio clips in total; after filtering out spoken-content clips, 340k non-speech clips remain (Yun et al., 29 May 2026). Environmental audio is mixed with clean speech at an SNR uniformly sampled between 2 and 10 dB, and mixing is skipped with probability 0.15 so that the model also sees speech-only examples (Yun et al., 29 May 2026). This yields a scalable training setup without requiring a large corpus of naturally paired immersive recordings.

At inference, ImmersiveTTS uses dual classifier-free guidance by independently masking content and environment prompts during training with probability 0.1 and then combining guided velocities at sampling time (Yun et al., 29 May 2026). Euler integration is used to solve the flow ODE, with $y_{\text{env}}$6 in the reported inference setting (Yun et al., 29 May 2026). The generated latent is decoded by the frozen AudioLDM2 VAE decoder and a pretrained vocoder to produce the final waveform (Yun et al., 29 May 2026).

5. Empirical performance

ImmersiveTTS is evaluated against VoiceLDM and VoiceDiT on environment-aware TTS benchmarks built from AudioCaps and from an augmented Seed-TTS plus AudioCaps mixture (Yun et al., 29 May 2026). On the AudioCaps test set, it reports SN-MOS $y_{\text{env}}$7, EC-MOS $y_{\text{env}}$8, ON-MOS $y_{\text{env}}$9, WER $X_{\text{wav}}$0, FAD $X_{\text{wav}}$1, CLAP $X_{\text{wav}}$2, and 25 NFEs, outperforming both baselines in speech naturalness, overall integration naturalness, intelligibility, and audio fidelity while using substantially fewer sampling steps (Yun et al., 29 May 2026). On the augmented Seed-TTS + AudioCaps test set, it reports SN-MOS $X_{\text{wav}}$3, EC-MOS $X_{\text{wav}}$4, ON-MOS $X_{\text{wav}}$5, WER $X_{\text{wav}}$6, FAD $X_{\text{wav}}$7, CLAP $X_{\text{wav}}$8, and 25 NFEs (Yun et al., 29 May 2026).

The ablation results identify domain-specific representation alignment as a principal source of the gains. Without REPA, the model yields WER $X_{\text{wav}}$9, FAD $X_{\text{mel}} \in \mathbb{R}^{F \times L}$0, and CLAP $X_{\text{mel}} \in \mathbb{R}^{F \times L}$1; with WavLM only, WER improves more than scene metrics; with ATST only, CLAP improves but WER degrades; with WavLM + ATST, the system reaches WER $X_{\text{mel}} \in \mathbb{R}^{F \times L}$2, FAD $X_{\text{mel}} \in \mathbb{R}^{F \times L}$3, and CLAP $X_{\text{mel}} \in \mathbb{R}^{F \times L}$4 (Yun et al., 29 May 2026). The best placement of the alignment losses is inside the MM-DiT blocks rather than only after multimodal fusion, which supports the claim that semantic correction is most useful during cross-modal interaction (Yun et al., 29 May 2026).

The guidance study shows a tension between transcript fidelity and environmental realism. Increasing $X_{\text{mel}} \in \mathbb{R}^{F \times L}$5 above 3 sharply hurts intelligibility, while increasing $X_{\text{mel}} \in \mathbb{R}^{F \times L}$6 can lower WER up to a point but degrades FAD and CLAP (Yun et al., 29 May 2026). A separate sampling-step analysis reports that with only 9 steps ImmersiveTTS already matches or exceeds VoiceLDM and VoiceDiT at 200 steps on WER, FAD, and CLAP, indicating that the flow-matching design is also an efficiency contribution (Yun et al., 29 May 2026).

6. Interpretive context and limitations

ImmersiveTTS occupies one region of a broader immersive speech design space. Earlier VTTS systems such as ViT-TTS, MS$X_{\text{mel}} \in \mathbb{R}^{F \times L}$7KU-VTTS, and M$X_{\text{mel}} \in \mathbb{R}^{F \times L}$8SE-VTTS focused on scene-consistent reverberant speech derived from RGB images, depth, speaker position, and semantic captions, but they did not claim full binaural or multichannel spatial audio rendering (Liu et al., 2023, He et al., 2024, Liu et al., 2024). AudioSpa studied text-guided binaural audio generation with a monaural reference waveform and showed that language can control source direction in binaural output, but it did not solve text-to-speech generation itself (Feng et al., 16 Feb 2025). This suggests that “immersive” in current literature spans at least three partially distinct axes: environment-consistent reverberation, joint speech-plus-background generation, and binaural spatialization.

The ImmersiveTTS paper is explicit about several limitations. Its training data are primarily synthetic mixtures of clean speech and environmental clips rather than naturally recorded immersive scenes, so real-world reverberation, mutual masking, and authentic scene acoustics remain only partially modeled (Yun et al., 29 May 2026). The authors also identify limited exploration of robustness under varying SNR and scene difficulty, limited paralinguistic control over prosody, speaking style, and emotion, and the broader trade-off that a unified model may still underperform specialized separate TTS or TTA systems on isolated single-task metrics (Yun et al., 29 May 2026).

Adjacent work makes those open problems more concrete. "Borderless Long Speech Synthesis" emphasizes long-horizon scene semantics, overlapping speech, and a structured semantic interface between an LLM agent and the synthesis engine, but it is optimized for offline content creation rather than real-time interaction (Song et al., 20 Mar 2026). "DeepASMR" shows that immersive speech can also mean intimate, low-intensity, partially unvoiced style control rather than environmental context alone (Zhang et al., 22 Jan 2026). "Deep Dubbing" targets audiobook immersion through text-to-timbre generation and context-aware instruct-TTS rather than environmental audio co-generation (Dai et al., 19 Sep 2025). "TouchTTS" addresses scalable data processing, streaming, and unified TTS/ASR deployment, but not room acoustics, spatial rendering, or environmental grounding (Song et al., 2024).

Taken together, these results frame ImmersiveTTS as a specific synthesis of environment-aware TTS and multimodal audio generation: it is not simply “TTS with background sound,” and it is not yet a full 3D auditory scene renderer. Its significance lies in formalizing environment-aware TTS as a genuinely joint problem and in showing that transcript alignment, multimodal joint attention, and domain-specific representation alignment can improve naturalness, intelligibility, and speech-environment integration within a single generative model (Yun et al., 29 May 2026).