ImmersiveTTS: Environment-Aware Text-to-Speech with Multimodal Diffusion Transformer and Domain-Specific Representation Alignment
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 diffusion transformer and fuses transcript-aligned speech latent with text-conditioned environmental context via joint attention. To enhance semantic consistency, we introduce a domain-specific representation alignment objective tailored to environment-aware TTS, leveraging complementary self-supervised representations from speech and audio encoders. Experimental results show that ImmersiveTTS achieves higher naturalness, intelligibility, and audio fidelity than existing approaches across objective metrics and human listening tests.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents ImmersiveTTS, an innovative environment-aware TTS framework that effectively synthesizes speech in context with environmental audio, significantly advancing the state of the art in multimodal audio generation.
Methodology
The methodology is robust, leveraging a multimodal diffusion transformer architecture to integrate speech and environmental audio effectively. The proposed domain-specific representation alignment objective is innovative, addressing the challenges of cross-modal interaction and enhancing semantic coherence. The dual-stream design allows for effective modeling of both speech and environmental contexts, which is a significant advancement in TTS systems.
Results & evaluation
The experimental evaluation is thorough, utilizing both subjective (MOS) and objective metrics (WER, FAD, CLAP) to assess the model's performance. The results demonstrate significant improvements over existing methods, particularly in naturalness and intelligibility, validating the proposed approach. The use of diverse datasets and comprehensive ablation studies further strengthens the evaluation.
Reproducibility
The paper provides detailed implementation information, including training setups, datasets, and evaluation metrics, which facilitates reproducibility. The availability of code on GitHub enhances this aspect, allowing other researchers to validate and build upon the work.
Limitations
The paper acknowledges limitations, such as reliance on synthetic mixtures of speech and environmental audio, which may not capture real-world complexities. Additionally, it notes the need for further exploration of robustness across varying SNR conditions and the lack of control over paralinguistic attributes like prosody and emotion.
Broader impact
The proposed system has significant implications for applications in immersive audio experiences, virtual reality, and interactive media. By improving the integration of speech and environmental sounds, it can enhance user engagement and realism in various applications, from gaming to assistive technologies. The paper presents ImmersiveTTS, an innovative environment-aware TTS framework that effectively synthesizes speech in context with environmental audio, significantly advancing the state of the art in multimodal audio generation.
Author of this paper? Send a correction.