ImmersiveTTS: Environment-Aware Text-to-Speech with Multimodal Diffusion Transformer and Domain-Specific Representation Alignment
arXiv:2605.30965v1 Announce Type: cross Abstract: Recent advancements in text-guided audio generation have yielded promising results in diverse domains, including sound effects, speech, and music. However, jointly generating speech with environmental audio remains challenging due to the inherent disparities in their acoustic patterns and temporal dynamics. We propose ImmersiveTTS, an environment-aware text-to-speech (TTS) model that generates natural speech seamlessly integrated within environmental contexts by explicitly modeling cross-modal interactions. Our model builds on a multimodal diff
The proliferation of advanced neural networks for audio generation is naturally leading to more complex, multimodal challenges such as integrating speech with environmental sounds. This advancement reflects ongoing progress in diffusion models and multimodal AI architectures.
This development pushes the frontier of AI's ability to create realistic and context-aware audio, which is crucial for immersive digital experiences, advanced virtual assistants, and sophisticated content generation.
The ability to seamlessly integrate speech within diverse environmental audio contexts moves beyond generating isolated speech or sound effects, enabling more dynamic and believable AI-generated audio scenarios.
- · AI developers
- · Gaming industry
- · Content creators
- · Virtual reality sector
- · Generative AI models limited to single modalities
More realistic and contextually appropriate AI-generated audio for various applications, including virtual environments and assistive technologies.
Increased demand for processing power and specialized datasets to train and deploy such complex multimodal models effectively.
Ethical considerations around the potential for highly realistic synthesized audio to be used in misrepresentation or deepfakes become more pronounced.
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Read at arXiv cs.AI